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Data Exploration Case Study: Credit Default

Exploratory data analysis is the main task of a Data Scientist with as much as 60% of their time being devoted to this task. As such, the majority of their time is spent on something that is rather boring compared to building models.

This post will provide a simple example of how to analyze a dataset from the website called Kaggle. This dataset is looking at how is likely to default on their credit. The following steps will be conducted in this analysis.

  1. Load the libraries and dataset
  2. Deal with missing data
  3. Some descriptive stats
  4. Normality check
  5. Model development

This is not an exhaustive analysis but rather a simple one for demonstration purposes. The dataset is available here

Load Libraries and Data

Here are some packages we will need

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import norm
from sklearn import tree
from scipy import stats
from sklearn import metrics

You can load the data with the code below

df_train=pd.read_csv('/application_train.csv')

You can examine what variables are available with the code below. This is not displayed here because it is rather long

df_train.columns
df_train.head()

Missing Data

I prefer to deal with missing data first because missing values can cause errors throughout the analysis if they are not dealt with immediately. The code below calculates the percentage of missing data in each column.

total=df_train.isnull().sum().sort_values(ascending=False)
percent=(df_train.isnull().sum()/df_train.isnull().count()).sort_values(ascending=False)
missing_data=pd.concat([total,percent],axis=1,keys=['Total','Percent'])
missing_data.head()
 
                           Total   Percent
COMMONAREA_MEDI           214865  0.698723
COMMONAREA_AVG            214865  0.698723
COMMONAREA_MODE           214865  0.698723
NONLIVINGAPARTMENTS_MODE  213514  0.694330
NONLIVINGAPARTMENTS_MEDI  213514  0.694330

Only the first five values are printed. You can see that some variables have a large amount of missing data. As such, they are probably worthless for inclusion in additional analysis. The code below removes all variables with any missing data.

pct_null = df_train.isnull().sum() / len(df_train)
missing_features = pct_null[pct_null > 0.0].index
df_train.drop(missing_features, axis=1, inplace=True)

You can use the .head() function if you want to see how  many variables are left.

Data Description & Visualization

For demonstration purposes, we will print descriptive stats and make visualizations of a few of the variables that are remaining.

round(df_train['AMT_CREDIT'].describe())
Out[8]: 
count     307511.0
mean      599026.0
std       402491.0
min        45000.0
25%       270000.0
50%       513531.0
75%       808650.0
max      4050000.0

sns.distplot(df_train['AMT_CREDIT']

1.png

round(df_train['AMT_INCOME_TOTAL'].describe())
Out[10]: 
count       307511.0
mean        168798.0
std         237123.0
min          25650.0
25%         112500.0
50%         147150.0
75%         202500.0
max      117000000.0
sns.distplot(df_train['AMT_INCOME_TOTAL']

1.png

I think you are getting the point. You can also look at categorical variables using the groupby() function.

We also need to address categorical variables in terms of creating dummy variables. This is so that we can develop a model in the future. Below is the code for dealing with all the categorical  variables and converting them to dummy variable’s

df_train.groupby('NAME_CONTRACT_TYPE').count()
dummy=pd.get_dummies(df_train['NAME_CONTRACT_TYPE'])
df_train=pd.concat([df_train,dummy],axis=1)
df_train=df_train.drop(['NAME_CONTRACT_TYPE'],axis=1)

df_train.groupby('CODE_GENDER').count()
dummy=pd.get_dummies(df_train['CODE_GENDER'])
df_train=pd.concat([df_train,dummy],axis=1)
df_train=df_train.drop(['CODE_GENDER'],axis=1)

df_train.groupby('FLAG_OWN_CAR').count()
dummy=pd.get_dummies(df_train['FLAG_OWN_CAR'])
df_train=pd.concat([df_train,dummy],axis=1)
df_train=df_train.drop(['FLAG_OWN_CAR'],axis=1)

df_train.groupby('FLAG_OWN_REALTY').count()
dummy=pd.get_dummies(df_train['FLAG_OWN_REALTY'])
df_train=pd.concat([df_train,dummy],axis=1)
df_train=df_train.drop(['FLAG_OWN_REALTY'],axis=1)

df_train.groupby('NAME_INCOME_TYPE').count()
dummy=pd.get_dummies(df_train['NAME_INCOME_TYPE'])
df_train=pd.concat([df_train,dummy],axis=1)
df_train=df_train.drop(['NAME_INCOME_TYPE'],axis=1)

df_train.groupby('NAME_EDUCATION_TYPE').count()
dummy=pd.get_dummies(df_train['NAME_EDUCATION_TYPE'])
df_train=pd.concat([df_train,dummy],axis=1)
df_train=df_train.drop(['NAME_EDUCATION_TYPE'],axis=1)

df_train.groupby('NAME_FAMILY_STATUS').count()
dummy=pd.get_dummies(df_train['NAME_FAMILY_STATUS'])
df_train=pd.concat([df_train,dummy],axis=1)
df_train=df_train.drop(['NAME_FAMILY_STATUS'],axis=1)

df_train.groupby('NAME_HOUSING_TYPE').count()
dummy=pd.get_dummies(df_train['NAME_HOUSING_TYPE'])
df_train=pd.concat([df_train,dummy],axis=1)
df_train=df_train.drop(['NAME_HOUSING_TYPE'],axis=1)

df_train.groupby('ORGANIZATION_TYPE').count()
dummy=pd.get_dummies(df_train['ORGANIZATION_TYPE'])
df_train=pd.concat([df_train,dummy],axis=1)
df_train=df_train.drop(['ORGANIZATION_TYPE'],axis=1)

You have to be careful with this because now you have many variables that are not necessary. For every categorical variable you must remove at least one category in order for the model to work properly.  Below we did this manually.

df_train=df_train.drop(['Revolving loans','F','XNA','N','Y','SK_ID_CURR,''Student','Emergency','Lower secondary','Civil marriage','Municipal apartment'],axis=1)

Below are some boxplots with the target variable and other variables in the dataset.

f,ax=plt.subplots(figsize=(8,6))
fig=sns.boxplot(x=df_train['TARGET'],y=df_train['AMT_INCOME_TOTAL'])

1.png

There is a clear outlier there. Below is another boxplot with a different variable

f,ax=plt.subplots(figsize=(8,6))
fig=sns.boxplot(x=df_train['TARGET'],y=df_train['CNT_CHILDREN'])

2

It appears several people have more than 10 children. This is probably a typo.

Below is a correlation matrix using a heatmap technique

corrmat=df_train.corr()
f,ax=plt.subplots(figsize=(12,9))
sns.heatmap(corrmat,vmax=.8,square=True)

1.png

The heatmap is nice but it is hard to really appreciate what is happening. The code below will sort the correlations from least to strongest, so we can remove high correlations.

c = df_train.corr().abs()

s = c.unstack()
so = s.sort_values(kind="quicksort")
print(so.head())

FLAG_DOCUMENT_12 FLAG_MOBIL 0.000005
FLAG_MOBIL FLAG_DOCUMENT_12 0.000005
Unknown FLAG_MOBIL 0.000005
FLAG_MOBIL Unknown 0.000005
Cash loans FLAG_DOCUMENT_14 0.000005

The list is to long to show here but the following variables were removed for having a high correlation with other variables.

df_train=df_train.drop(['WEEKDAY_APPR_PROCESS_START','FLAG_EMP_PHONE','REG_CITY_NOT_WORK_CITY','REGION_RATING_CLIENT','REG_REGION_NOT_WORK_REGION'],axis=1)

Below we check a few variables for homoscedasticity, linearity, and normality  using plots and histograms

sns.distplot(df_train['AMT_INCOME_TOTAL'],fit=norm)
fig=plt.figure()
res=stats.probplot(df_train['AMT_INCOME_TOTAL'],plot=plt)

12

This is not normal

sns.distplot(df_train['AMT_CREDIT'],fit=norm)
fig=plt.figure()
res=stats.probplot(df_train['AMT_CREDIT'],plot=plt)

12

This is not normal either. We could do transformations, or we can make a non-linear model instead.

Model Development

Now comes the easy part. We will make a decision tree using only some variables to predict the target. In the code below we make are X and y dataset.

X=df_train[['Cash loans','DAYS_EMPLOYED','AMT_CREDIT','AMT_INCOME_TOTAL','CNT_CHILDREN','REGION_POPULATION_RELATIVE']]
y=df_train['TARGET']

The code below fits are model and makes the predictions

clf=tree.DecisionTreeClassifier(min_samples_split=20)
clf=clf.fit(X,y)
y_pred=clf.predict(X)

Below is the confusion matrix followed by the accuracy

print (pd.crosstab(y_pred,df_train['TARGET']))
TARGET       0      1
row_0                
0       280873  18493
1         1813   6332
accuracy_score(y_pred,df_train['TARGET'])
Out[47]: 0.933966589813047

Lastly, we can look at the precision, recall, and f1 score

print(metrics.classification_report(y_pred,df_train['TARGET']))
              precision    recall  f1-score   support

           0       0.99      0.94      0.97    299366
           1       0.26      0.78      0.38      8145

   micro avg       0.93      0.93      0.93    307511
   macro avg       0.62      0.86      0.67    307511
weighted avg       0.97      0.93      0.95    307511

This model looks rather good in terms of accuracy of the training set. It actually impressive that we could use so few variables from such a large dataset and achieve such a high degree of accuracy.

Conclusion

Data exploration and analysis is the primary task of a data scientist.  This post was just an example of how this can be approached. Of course, there are many other creative ways to do this but the simplistic nature of this analysis yielded strong results

Hierarchical Regression in R

In this post, we will learn how to conduct a hierarchical regression analysis in R. Hierarchical regression analysis is used in situation in which you want to see if adding additional variables to your model will significantly change the r2 when accounting for the other variables in the model. This approach is a model comparison approach and not necessarily a statistical one.

We are going to use the “Carseats” dataset from the ISLR package. Our goal will be to predict total sales using the following independent variables in three different models.

model 1 = intercept only
model 2 = Sales~Urban + US + ShelveLoc
model 3 = Sales~Urban + US + ShelveLoc + price + income
model 4 = Sales~Urban + US + ShelveLoc + price + income + Advertising

Often the primary goal with hierarchical regression is to show that the addition of a new variable builds or improves upon a previous model in a statistically significant way. For example, if a previous model was able to predict the total sales of an object using three variables you may want to see if a new additional variable you have in mind may improve model performance. Another way to see this is in the following research question

Is a model that explains the total sales of an object with Urban location, US location, shelf location, price, income and advertising cost as independent variables superior in terms of R2 compared to a model that explains total sales with Urban location, US location, shelf location, price and income as independent variables?

In this complex research question we essentially want to know if adding advertising cost will improve the model significantly in terms of the r square. The formal steps that we will following to complete this analysis is as follows.

  1. Build sequential (nested) regression models by adding variables at each step.
  2. Run ANOVAs in order to compute the R2
  3. Compute difference in sum of squares for each step
    1. Check F-statistics and p-values for the SS differences.
  4. Compare sum of squares between models from ANOVA results.
  5. Compute increase in R2 from sum of square difference
  6. Run regression to obtain the coefficients for each independent variable.

We will now begin our analysis. Below is some initial code

library(ISLR)
data("Carseats")

Model Development

We now need to create our models. Model 1 will not have any variables in it and will be created for the purpose of obtaining the total sum of squares. Model 2 will include demographic variables. Model 3 will contain the initial model with the continuous independent variables. Lastly, model 4 will contain all the information of the previous models with the addition of the continuous independent variable of advertising cost. Below is the code.

model1 = lm(Sales~1,Carseats)
model2=lm(Sales~Urban + US + ShelveLoc,Carseats)
model3=lm(Sales~Urban + US + ShelveLoc + Price + Income,Carseats)
model4=lm(Sales~Urban + US + ShelveLoc + Price + Income + Advertising,Carseats)

We can now turn to the ANOVA analysis for model comparison #ANOVA Calculation We will use the anova() function to calculate the total sum of square for model 0. This will serve as a baseline for the other models for calculating r square

anova(model1,model2,model3,model4)
## Analysis of Variance Table
## 
## Model 1: Sales ~ 1
## Model 2: Sales ~ Urban + US + ShelveLoc
## Model 3: Sales ~ Urban + US + ShelveLoc + Price + Income
## Model 4: Sales ~ Urban + US + ShelveLoc + Price + Income + Advertising
##   Res.Df    RSS Df Sum of Sq       F    Pr(>F)    
## 1    399 3182.3                                   
## 2    395 2105.4  4   1076.89  89.165 < 2.2e-16 ***
## 3    393 1299.6  2    805.83 133.443 < 2.2e-16 ***
## 4    392 1183.6  1    115.96  38.406 1.456e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

For now, we are only focusing on the residual sum of squares. Here is a basic summary of what we know as we compare the models.

model 1 = sum of squares = 3182.3
model 2 = sum of squares = 2105.4 (with demographic variables of Urban, US, and ShelveLoc)
model 3 = sum of squares = 1299.6 (add price and income)
model 4 = sum of squares = 1183.6 (add Advertising)

Each model is statistical significant which means adding each variable lead to some improvement.

By adding price and income to the model we were able to improve the model in a statistically significant way. The r squared increased by .25 below is how this was calculated.

2105.4-1299.6 #SS of Model 2 - Model 3
## [1] 805.8
805.8/ 3182.3 #SS difference of Model 2 and Model 3 divided by total sum of sqaure ie model 1
## [1] 0.2532131

When we add Advertising to the model the r square increases by .03. The calculation is below

1299.6-1183.6 #SS of Model 3 - Model 4
## [1] 116
116/ 3182.3 #SS difference of Model 3 and Model 4 divided by total sum of sqaure ie model 1
## [1] 0.03645162

Coefficients and R Square

We will now look at a summary of each model using the summary() function.

summary(model2)
## 
## Call:
## lm(formula = Sales ~ Urban + US + ShelveLoc, data = Carseats)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.713 -1.634 -0.019  1.738  5.823 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       4.8966     0.3398  14.411  < 2e-16 ***
## UrbanYes          0.0999     0.2543   0.393   0.6947    
## USYes             0.8506     0.2424   3.510   0.0005 ***
## ShelveLocGood     4.6400     0.3453  13.438  < 2e-16 ***
## ShelveLocMedium   1.8168     0.2834   6.410 4.14e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.309 on 395 degrees of freedom
## Multiple R-squared:  0.3384, Adjusted R-squared:  0.3317 
## F-statistic: 50.51 on 4 and 395 DF,  p-value: < 2.2e-16
summary(model3)
## 
## Call:
## lm(formula = Sales ~ Urban + US + ShelveLoc + Price + Income, 
##     data = Carseats)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.9096 -1.2405 -0.0384  1.2754  4.7041 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     10.280690   0.561822  18.299  < 2e-16 ***
## UrbanYes         0.219106   0.200627   1.092    0.275    
## USYes            0.928980   0.191956   4.840 1.87e-06 ***
## ShelveLocGood    4.911033   0.272685  18.010  < 2e-16 ***
## ShelveLocMedium  1.974874   0.223807   8.824  < 2e-16 ***
## Price           -0.057059   0.003868 -14.752  < 2e-16 ***
## Income           0.013753   0.003282   4.190 3.44e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.818 on 393 degrees of freedom
## Multiple R-squared:  0.5916, Adjusted R-squared:  0.5854 
## F-statistic: 94.89 on 6 and 393 DF,  p-value: < 2.2e-16
summary(model4)
## 
## Call:
## lm(formula = Sales ~ Urban + US + ShelveLoc + Price + Income + 
##     Advertising, data = Carseats)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.2199 -1.1703  0.0225  1.0826  4.1124 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     10.299180   0.536862  19.184  < 2e-16 ***
## UrbanYes         0.198846   0.191739   1.037    0.300    
## USYes           -0.128868   0.250564  -0.514    0.607    
## ShelveLocGood    4.859041   0.260701  18.638  < 2e-16 ***
## ShelveLocMedium  1.906622   0.214144   8.903  < 2e-16 ***
## Price           -0.057163   0.003696 -15.467  < 2e-16 ***
## Income           0.013750   0.003136   4.384 1.50e-05 ***
## Advertising      0.111351   0.017968   6.197 1.46e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.738 on 392 degrees of freedom
## Multiple R-squared:  0.6281, Adjusted R-squared:  0.6214 
## F-statistic: 94.56 on 7 and 392 DF,  p-value: < 2.2e-16

You can see for yourself the change in the r square. From model 2 to model 3 there is a 26 point increase in r square just as we calculated manually. From model 3 to model 4 there is a 3 point increase in r square. The purpose of the anova() analysis was determined if the significance of the change meet a statistical criterion, The lm() function reports a change but not the significance of it.

Conclusion

Hierarchical regression is just another potential tool for the statistical researcher. It provides you with a way to develop several models and compare the results based on any potential improvement in the r square.

Gradient Boosting Classification in Python

Gradient Boosting is an alternative form of boosting to AdaBoost. Many consider gradient boosting to be a better performer than adaboost. Some differences between the two algorithms is that gradient boosting uses optimization for weight the estimators. Like adaboost, gradient boosting can be used for most algorithms but is commonly associated with decision trees.

In addition, gradient boosting requires several additional hyperparameters such as max depth and subsample. Max depth has to do with the number of nodes in a tree. The higher the number the purer the classification become. The downside to this is the risk of overfitting.

Subsampling has to do with the proportion of the sample that is used for each estimator. This can range from a decimal value up until the whole number 1. If the value is set to 1 it becomes stochastic gradient boosting.

This post is focused on classification. To do this, we will use the cancer dataset from the pydataset library. Our goal will be to predict the status of patients (alive or dead) using the available independent variables. The steps we will use are as follows.

  1. Data preparation
  2. Baseline decision tree model
  3. Hyperparameter tuning
  4. Gradient boosting model development

Below is some initial code.

from sklearn.ensemble import GradientBoostingClassifier
from sklearn import tree
from sklearn.model_selection import GridSearchCV
import numpy as np
from pydataset import data
import pandas as pd
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold

Data Preparation

The data preparation is simple in this situtation. All we need to do is load are dataset, dropping missing values, and create our X dataset and y dataset. All this happens in the code below.

df=data('cancer').dropna()
X=df[['time','sex','ph.karno','pat.karno','meal.cal','wt.loss']]
y=df['status']

We will now develop our baseline decision tree model.

Baseline Model

The purpose of the baseline model is to have something to compare our gradient boosting model to. The strength of a model is always relative to some other model, so we need to make at least two, so we can say one is better than the other.

The criteria for better in this situation is accuracy. Therefore, we will make a decision tree model, but we will manipulate the max depth of the tree to create 9 different baseline models. The best accuracy model will be the baseline model.

To achieve this, we need to use a for loop to make python make several decision trees. We also need to set the parameters for the cross validation by calling KFold(). Once this is done, we print the results for the 9 trees. Below is the code and results.

crossvalidation=KFold(n_splits=10,shuffle=True,random_state=1)
for depth in range (1,10):
tree_classifier=tree.DecisionTreeClassifier(max_depth=depth,random_state=1)
if tree_classifier.fit(X,y).tree_.max_depth<depth:
break
score=np.mean(cross_val_score(tree_classifier,X,y,scoring='accuracy', cv=crossvalidation,n_jobs=1))
print(depth, score)
1 0.71875
2 0.6477941176470589
3 0.6768382352941177
4 0.6698529411764707
5 0.6584558823529412
6 0.6525735294117647
7 0.6283088235294118
8 0.6573529411764706
9 0.6577205882352941

It appears that when the max depth is limited to 1 that we get the best accuracy at almost 72%. This will be our baseline for comparison. We will now tune the parameters for the gradient boosting algorithm

Hyperparameter Tuning

There are several hyperparameters we need to tune. The ones we will tune are as follows

  • number of estimators
  • learning rate
  • subsample
  • max depth

First, we will create an instance of the gradient boosting classifier. Second, we will create our grid for the search. It is inside this grid that we set several values for each hyperparameter. Then we call GridSearchCV and place the instance of the gradient boosting classifier, the grid, the cross validation values from mad earlier, and n_jobs all together in one place. Below is the code for this.

GBC=GradientBoostingClassifier()
search_grid={'n_estimators':[500,1000,2000],'learning_rate':[.001,0.01,.1],'max_depth':[1,3,5],'subsample':[.5,.75,1],'random_state':[1]}
search=GridSearchCV(estimator=GBC,param_grid=search_grid,scoring='accuracy',n_jobs=1,cv=crossvalidation)

You can now run your model by calling .fit(). Keep in mind that there are several hyperparameters. This means that it might take some time to run the calculations. It is common to find values for max depth, subsample, and number of estimators first. Then as second run through is done to find the learning rate. In our example, we are doing everything at once which is why it takes longer. Below is the code with the out for best parameters and best score.

search.fit(X,y)
search.best_params_
Out[11]:
{'learning_rate': 0.01,
'max_depth': 5,
'n_estimators': 2000,
'random_state': 1,
'subsample': 0.75}
search.best_score_
Out[12]: 0.7425149700598802

You can see what the best hyperparameters are for yourself. In addition, we see that when these parameters were set we got an accuracy of 74%. This is superior to our baseline model. We will now see if we can replicate these numbers when we use them for our Gradient Boosting model.

Gradient Boosting Model

Below is the code and results for the model with the predetermined hyperparameter values.

ada2=GradientBoostingClassifier(n_estimators=2000,learning_rate=0.01,subsample=.75,max_depth=5,random_state=1)
score=np.mean(cross_val_score(ada2,X,y,scoring='accuracy',cv=crossvalidation,n_jobs=1))
score
Out[17]: 0.742279411764706

You can see that the results are similar. This is just additional information that the gradient boosting model does outperform the baseline decision tree model.

Conclusion

This post provided an example of what gradient boosting classification can do for a model. With its distinct characteristics gradient boosting is generally a better performing boosting algorithm in comparison to AdaBoost.

AdaBoost Regression with Python

This post will share how to use the adaBoost algorithm for regression in Python. What boosting does is that it makes multiple models in a sequential manner. Each newer model tries to successful predict what older models struggled with. For regression, the average of the models are used for the predictions.  It is often most common to use boosting with decision trees but this approach can be used with any machine learning algorithm that deals with supervised learning.

Boosting is associated with ensemble learning because several models are created that are averaged together. An assumption of boosting, is that combining several weak models can make one really strong and accurate model.

For our purposes, we will be using adaboost classification to improve the performance of a decision tree in python. We will use the cancer dataset from the pydataset library. Our goal will be to predict the weight loss of a patient based on several independent variables. The steps of this process are as follows.

  1. Data preparation
  2. Regression decision tree baseline model
  3. Hyperparameter tuning of Adaboost regression model
  4. AdaBoost regression model development

Below is some initial code

from sklearn.ensemble import AdaBoostRegressor
from sklearn import tree
from sklearn.model_selection import GridSearchCV
import numpy as np
from pydataset import data
import pandas as pd
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error

Data Preparation

There is little data preparation for this example. All we need to do is load the data and create the X and y datasets. Below is the code.

df=data('cancer').dropna()
X=df[['time','sex','ph.karno','pat.karno','status','meal.cal']]
y=df['wt.loss']

We will now proceed to creating the baseline regression decision tree model.

Baseline Regression Tree Model

The purpose of the baseline model is for comparing it to the performance of our model that utilizes adaBoost. In order to make this model we need to Initiate a Kfold cross-validation. This will help in stabilizing the results. Next we will create a for loop so that we can create several trees that vary based on their depth. By depth, it is meant how far the tree can go to purify the classification. More depth often leads to a higher likelihood of overfitting.

Finally, we will then print the results for each tree. The criteria used for judgment is the mean squared error. Below is the code and results

for depth in range (1,10):
tree_regressor=tree.DecisionTreeRegressor(max_depth=depth,random_state=1)
if tree_regressor.fit(X,y).tree_.max_depth<depth:
break
score=np.mean(cross_val_score(tree_regressor,X,y,scoring='neg_mean_squared_error', cv=crossvalidation,n_jobs=1))
print(depth, score)
1 -193.55304528235052
2 -176.27520747356175
3 -209.2846723461564
4 -218.80238479654003
5 -222.4393459885871
6 -249.95330609042858
7 -286.76842138165705
8 -294.0290706405905
9 -287.39016236497804

Looks like a tree with a depth of 2 had the lowest amount of error. We can now move to tuning the hyperparameters for the adaBoost algorithm.

Hyperparameter Tuning

For hyperparameter tuning we need to start by initiating our AdaBoostRegresor() class. Then we need to create our grid. The grid will address two hyperparameters which are the number of estimators and the learning rate. The number of estimators tells Python how many models to make and the learning indicates how each tree contributes to the overall results. There is one more parameters which is random_state but this is just for setting the seed and never changes.

After making the grid, we need to use the GridSearchCV function to finish this process. Inside this function you have to set the estimator which is adaBoostRegressor, the parameter grid which we just made, the cross validation which we made when we created the baseline model, and the n_jobs which allocates resources for the calculation. Below is the code.

ada=AdaBoostRegressor()
search_grid={'n_estimators':[500,1000,2000],'learning_rate':[.001,0.01,.1],'random_state':[1]}
search=GridSearchCV(estimator=ada,param_grid=search_grid,scoring='neg_mean_squared_error',n_jobs=1,cv=crossvalidation)

Next, we can run the model with the desired grid in place. Below is the code for fitting the mode as well as the best parameters and the score to expect when using the best parameters.

search.fit(X,y)
search.best_params_
Out[31]: {'learning_rate': 0.01, 'n_estimators': 500, 'random_state': 1}
search.best_score_
Out[32]: -164.93176650920856

The best mix of hyperparameters is a learning rate of 0.01 and 500 estimators. This mix led to a mean error score of 164, which is a little lower than our single decision tree of 176. We will see how this works when we run our model with the refined hyperparameters.

AdaBoost Regression Model

Below is our model but this time with the refined hyperparameters.

ada2=AdaBoostRegressor(n_estimators=500,learning_rate=0.001,random_state=1)
score=np.mean(cross_val_score(ada2,X,y,scoring='neg_mean_squared_error',cv=crossvalidation,n_jobs=1))
score
Out[36]: -174.52604137201791

You can see the score is not as good but it is within reason.

Conclusion

In this post, we explored how to use the AdaBoost algorithm for regression. Employing this algorithm can help to strengthen a model in many ways at times.

Research Questions, Variables, and Statistics

Working with students over the years has led me to the conclusion that often students do not understand the connection between variables, quantitative research questions and the statistical tools

used to answer these questions. In other words, students will take statistics and pass the class. Then they will take research methods, collect data, and have no idea how to analyze the data even though they have the necessary skills in statistics to succeed.


This means that the students have a theoretical understanding of statistics but struggle in the application of it. In this post, we will look at some of the connections between research questions and statistics.

Variables

Variables are important because how they are measured affects the type of question you can ask and get answers to. Students often have no clue how they will measure a variable and therefore have no idea how they will answer any research questions they may have.

Another aspect that can make this confusing is that many variables can be measured more than one way. Sometimes the variable “salary” can be measured in a continuous manner or in a categorical manner. The superiority of one or the other depends on the goals of the research.

It is critical to support students to have a thorough understanding of variables in order to support their research.

Types of Research Questions

In general, there are two types of research questions. These two types are descriptive and relational questions. Descriptive questions involve the use of descriptive statistic such as the mean, median, mode, skew, kurtosis, etc. The purpose is to describe the sample quantitatively with numbers (ie the average height is 172cm) rather than relying on qualitative descriptions of it (ie the people are tall).

Below are several example research questions that are descriptive in nature.

  1. What is the average height of the participants in the study?
  2. What proportion of the sample is passed the exam?
  3. What are the respondents perceptions towards the cafeteria?

These questions are not intellectually sophisticated but they are all answerable with descriptive statistical tools. Question 1 can be answered by calculating the mean. Question 2 can be answered by determining how many passed the exam and dividing by the total sample size. Question 3 can be answered by calculating the mean of all the survey items that are used to measure respondents perception of the cafeteria.

Understanding the link between research question and statistical tool is critical. However, many people seem to miss the connection between the type of question and the tools to use.

Relational questions look for the connection or link between variables. Within this type there are two sub-types. Comparison question involve comparing groups. The other sub-type is called relational or an association question.

Comparison questions involve comparing groups on a continuous variable. For example, comparing men and women by height. What you want to know is whether there is a difference in the height of men and women. The comparison here is trying to determine if gender is related to height. Therefore, it is looking for a relationship just not in the way that many student understand. Common comparison questions include the following.male

  1. Is there a difference in height by gender among the participants?
  2. Is there a difference in reading scores by grade level?
  3. Is there a difference in job satisfaction in based on major?

Each of these questions can be answered using ANOVA or if we want to get technical and there are only two groups (ie gender) we can use t-test. This is a broad overview and does not include the complexities of one-sample test and or paired t-test.

Relational or association question involve continuous variables primarily. The goal is to see how variables move together. For example, you may look for the relationship between height and weight of students. Common questions include the following.

  1.  Is there a relationship between height and weight?
  2. Does height and show size explain weight?

Questions 1 can be answered by calculating the correlation. Question 2 requires the use of linear regression in order to answer the question.

Conclusion

The challenging as a teacher is showing the students the connection between statistics and research questions from the real world. It takes time for students to see how the question inspire the type of statistical tool to use. Understanding this is critical because it helps to frame the possibilities of what to do in research based on the statistical knowledge one has.

Elastic Net Regression in Python

Elastic net regression combines the power of ridge and lasso regression into one algorithm. What this means is that with elastic net the algorithm can remove weak variables altogether as with lasso or to reduce them to close to zero as with ridge. All of these algorithms are examples of regularized regression.

This post will provide an example of elastic net regression in Python. Below are the steps of the analysis.

  1. Data preparation
  2. Baseline model development
  3. Elastic net model development

To accomplish this, we will use the Fair dataset from the pydataset library. Our goal will be to predict marriage satisfaction based on the other independent variables. Below is some initial code to begin the analysis.

from pydataset import data
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 5000)
pd.set_option('display.max_columns', 5000)
pd.set_option('display.width', 10000)
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import ElasticNet
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

Data Preparation

We will now load our data. The only preparation that we need to do is convert the factor variables to dummy variables. Then we will make our  and y datasets. Below is the code.

df=pd.DataFrame(data('Fair'))
df.loc[df.sex== 'male', 'sex'] = 0
df.loc[df.sex== 'female','sex'] = 1
df['sex'] = df['sex'].astype(int)
df.loc[df.child== 'no', 'child'] = 0
df.loc[df.child== 'yes','child'] = 1
df['child'] = df['child'].astype(int)
X=df[['religious','age','sex','ym','education','occupation','nbaffairs']]
y=df['rate']

We can now proceed to creating the baseline model

Baseline Model

This model is a basic regression model for the purpose of comparison. We will instantiate our regression model, use the fit command and finally calculate the mean squared error of the data. The code is below.

regression=LinearRegression()
regression.fit(X,y)
first_model=(mean_squared_error(y_true=y,y_pred=regression.predict(X)))
print(first_model)
1.0498738644696668

This mean standard error score of 1.05 is our benchmark for determining if the elastic net model will be better or worst. Below are the coefficients of this first model. We use a for loop to go through the model and the zip function to combine the two columns.

coef_dict_baseline = {}
for coef, feat in zip(regression.coef_,X.columns):
coef_dict_baseline[feat] = coef
coef_dict_baseline
Out[63]:
{'religious': 0.04235281110639178,
'age': -0.009059645428673819,
'sex': 0.08882013337087094,
'ym': -0.030458802565476516,
'education': 0.06810255742293699,
'occupation': -0.005979506852998164,
'nbaffairs': -0.07882571247653956}

We will now move to making the elastic net model.

Elastic Net Model

Elastic net, just like ridge and lasso regression, requires normalize data. This argument  is set inside the ElasticNet function. The second thing we need to do is create our grid. This is the same grid as we create for ridge and lasso in prior posts. The only thing that is new is the l1_ratio argument.

When the l1_ratio is set to 0 it is the same as ridge regression. When l1_ratio is set to 1 it is lasso. Elastic net is somewhere between 0 and 1 when setting the l1_ratio. Therefore, in our grid, we need to set several values of this argument. Below is the code.

elastic=ElasticNet(normalize=True)
search=GridSearchCV(estimator=elastic,param_grid={'alpha':np.logspace(-5,2,8),'l1_ratio':[.2,.4,.6,.8]},scoring='neg_mean_squared_error',n_jobs=1,refit=True,cv=10)

We will now fit our model and display the best parameters and the best results we can get with that setup.

search.fit(X,y)
search.best_params_
Out[73]: {'alpha': 0.001, 'l1_ratio': 0.8}
abs(search.best_score_)
Out[74]: 1.0816514028705004

The best hyperparameters was an alpha set to 0.001 and a l1_ratio of 0.8. With these settings we got an MSE of 1.08. This is above our baseline model of MSE 1.05  for the baseline model. Which means that elastic net is doing worse than linear regression. For clarity, we will set our hyperparameters to the recommended values and run on the data.

elastic=ElasticNet(normalize=True,alpha=0.001,l1_ratio=0.75)
elastic.fit(X,y)
second_model=(mean_squared_error(y_true=y,y_pred=elastic.predict(X)))
print(second_model)
1.0566430678343806

Now our values are about the same. Below are the coefficients

coef_dict_baseline = {}
for coef, feat in zip(elastic.coef_,X.columns):
coef_dict_baseline[feat] = coef
coef_dict_baseline
Out[76]:
{'religious': 0.01947541724957858,
'age': -0.008630896492807691,
'sex': 0.018116464568090795,
'ym': -0.024224831274512956,
'education': 0.04429085595448633,
'occupation': -0.0,
'nbaffairs': -0.06679513627963515}

The coefficients are mostly the same. Notice that occupation was completely removed from the model in the elastic net version. This means that this values was no good to the algorithm. Traditional regression cannot do this.

Conclusion

This post provided an example of elastic net regression. Elastic net regression allows for the maximum flexibility in terms of finding the best combination of ridge and lasso regression characteristics. This flexibility is what gives elastic net its power.

Lasso Regression with Python

Lasso regression is another form of regularized regression. With this particular version, the coefficient of a variable can be reduced all the way to zero through the use of the l1 regularization. This is in contrast to ridge regression which never completely removes a variable from an equation as it employs l2 regularization.

Regularization helps to stabilize estimates as well as deal with bias and variance in a model. In this post, we will use the “CaSchools” dataset from the pydataset library. Our goal will be to predict test scores based on several independent variables. The steps we will follow are as follows.

  1. Data preparation
  2. Develop a baseline linear model
  3. Develop lasso regression model

The initial code is as follows

from pydataset import data
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
df=pd.DataFrame(data(‘Caschool’))

Data Preparation

The data preparation is simple in this example. We only have to store the desired variables in our X and y datasets. We are not using all of the variables. Some were left out because they were highly correlated. Lasso is able to deal with this to a certain extent w=but it was decided to leave them out anyway. Below is the code.

X=df[['teachers','calwpct','mealpct','compstu','expnstu','str','avginc','elpct']]
y=df['testscr']

Baseline Model

We can now run our baseline model. This will give us a measure of comparison for the lasso model. Our metric is the mean squared error. Below is the code with the results of the model.

regression=LinearRegression()
regression.fit(X,y)
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
first_model=(mean_squared_error(y_true=y,y_pred=regression.predict(X)))
print(first_model)
69.07380530137416

First, we instantiate the LinearRegression class. Then, we run the .fit method to do the analysis. Next, we predicted future values of our regression model and save the results to the object first_model. Lastly, we printed the results.

Below are the coefficient for the baseline regression model.

coef_dict_baseline = {}
for coef, feat in zip(regression.coef_,X.columns):
coef_dict_baseline[feat] = coef
coef_dict_baseline
Out[52]:
{'teachers': 0.00010011947964873427,
'calwpct': -0.07813766458116565,
'mealpct': -0.3754719080127311,
'compstu': 11.914006268826652,
'expnstu': 0.001525630709965126,
'str': -0.19234209691788984,
'avginc': 0.6211690806021222,
'elpct': -0.19857026121348267}

The for loop simply combines the features in our model with their coefficients. With this information we can now make our lasso model and compare the results.

Lasso Model

For our lasso model, we have to determine what value to set the l1 or alpha to prior to creating the model. This can be done with the grid function, This function allows you to assess several models with different l1 settings. Then python will tell which setting is the best. Below is the code.

lasso=Lasso(normalize=True)
search=GridSearchCV(estimator=lasso,param_grid={'alpha':np.logspace(-5,2,8)},scoring='neg_mean_squared_error',n_jobs=1,refit=True,cv=10)
search.fit(X,y)

We start be instantiate lasso with normalization set to true. It is important to scale data when doing regularized regression. Next, we setup our grid, we include the estimator, and parameter grid, and scoring. The alpha is set using logspace. We want values between -5 and 2, and we want 8 evenly spaced settings for the alpha. The other arguments include cv which stands for cross-validation. n_jobs effects processing and refit updates the parameters. 

After completing this, we used the fit function. The code below indicates the appropriate alpha and the expected score if we ran the model with this alpha setting.

search.best_params_
Out[55]: {'alpha': 1e-05}
abs(search.best_score_)
Out[56]: 85.38831122904011

`The alpha is set almost to zero, which is the same as a regression model. You can also see that the mean squared error is actually worse than in the baseline model. In the code below, we run the lasso model with the recommended alpha setting and print the results.

lasso=Lasso(normalize=True,alpha=1e-05)
lasso.fit(X,y)
second_model=(mean_squared_error(y_true=y,y_pred=lasso.predict(X)))
print(second_model)
69.0738055527604

The value for the second model is almost the same as the first one. The tiny difference is due to the fact that there is some penalty involved. Below are the coefficient values.

coef_dict_baseline = {}
for coef, feat in zip(lasso.coef_,X.columns):
coef_dict_baseline[feat] = coef
coef_dict_baseline
Out[63]:
{'teachers': 9.795933425676567e-05,
'calwpct': -0.07810938255735576,
'mealpct': -0.37548182158171706,
'compstu': 11.912164626067028,
'expnstu': 0.001525439984250718,
'str': -0.19225486069458508,
'avginc': 0.6211695477945162,
'elpct': -0.1985510490295491}

The coefficient values are also slightly different. The only difference is the teachers variable was essentially set to zero. This means that it is not a useful variable for predicting testscrs. That is ironic to say the least.

Conclusion

Lasso regression is able to remove variables that are not adequate predictors of the outcome variable. Doing this in Python  is fairly simple. This yet another tool that can be used in statistical analysis.

Ridge Regression in Python

Ridge regression is one of several regularized linear models. Regularization is the process of penalizing coefficients of variables either by removing them and or reduce their impact. Ridge regression reduces the effect of problematic variables close to zero but never fully removes them.

We will go through an example of ridge regression using the VietNamI dataset available in the pydataset library. Our goal will be to predict expenses based on the variables available. We will complete this task using the following steps/

  1. Data preparation
  2. Baseline model development
  3. Ridge regression model

Below is the initial code

from pydataset import data
import numpy as np
import pandas as pd
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Ridge
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_erro

Data Preparation

The data preparation is simple. All we have to do is load the data and convert the sex variable to a dummy variable. We also need to set up our X and y datasets. Below is the code.

df=pd.DataFrame(data('VietNamI'))
df.loc[df.sex== 'male', 'sex'] = 0
df.loc[df.sex== 'female','sex'] = 1
df['sex'] = df['sex'].astype(int)
X=df[['pharvis','age','sex','married','educ','illness','injury','illdays','actdays','insurance']]
y=df['lnhhexp'

We can now create our baseline regression model.

Baseline Model

The metric we are using is the mean squared error. Below is the code and output for our baseline regression model. This is a model that has no regularization to it. Below is the code.

regression=LinearRegression()
regression.fit(X,y)
first_model=(mean_squared_error(y_true=y,y_pred=regression.predict(X)))
print(first_model)
0.35528915032173053

This  value of 0.355289 will be our indicator to determine if the regularized ridge regression model is superior or not.

Ridge Model

In order to create our ridge model we need to first determine the most appropriate value for the l2 regularization. L2 is the name of the hyperparameter that is used in ridge regression. Determining the value of a hyperparameter requires the use of a grid. In the code below, we first are ridge model and indicate normalization in order to get better estimates. Next we setup the grid that we will use. Below is the code.

ridge=Ridge(normalize=True)
search=GridSearchCV(estimator=ridge,param_grid={'alpha':np.logspace(-5,2,8)},scoring='neg_mean_squared_error',n_jobs=1,refit=True,cv=10)

The search object has several arguments within it. Alpha is hyperparameter we are trying to set. The log space is the range of values we want to test. We want the log of -5 to 2, but we only get 8 values from within that range evenly spread out. Are metric is the mean squared error. Refit set true means to adjust the parameters while modeling and cv is the number of folds to develop for the cross-validation. We can now use the .fit function to run the model and then use the .best_params_ and .best_scores_ function to determine the model;s strength. Below is the code.

search.fit(X,y)
search.best_params_
{'alpha': 0.01}
abs(search.best_score_)
0.3801489007094425

The best_params_ tells us what to set alpha too which in this case is 0.01. The best_score_ tells us what the best possible mean squared error is. In this case, the value of 0.38 is worse than what the baseline model was. We can confirm this by  fitting our model with the ridge information and finding the mean squared error. This is done below.

ridge=Ridge(normalize=True,alpha=0.01)
ridge.fit(X,y)
second_model=(mean_squared_error(y_true=y,y_pred=ridge.predict(X)))
print(second_model)
0.35529321992606566

The 0.35 is lower than the 0.38. This is because the last results are not cross-validated. In addition, these results indicate that there is little difference between the ridge and baseline models. This is confirmed with the coefficients of each model found below.

coef_dict_baseline = {}
for coef, feat in zip(regression.coef_,data("VietNamI").columns):
coef_dict_baseline[feat] = coef
coef_dict_baseline
Out[188]:
{'pharvis': 0.013282050886950674,
'lnhhexp': 0.06480086550467873,
'age': 0.004012412278795848,
'sex': -0.08739614349708981,
'married': 0.075276463838362,
'educ': -0.06180921300600292,
'illness': 0.040870384578962596,
'injury': -0.002763768716569026,
'illdays': -0.006717063310893158,
'actdays': 0.1468784364977112}


coef_dict_ridge = {}
for coef, feat in zip(ridge.coef_,data("VietNamI").columns):
coef_dict_ridge[feat] = coef
coef_dict_ridge
Out[190]:
{'pharvis': 0.012881937698185289,
'lnhhexp': 0.06335455237380987,
'age': 0.003896623321297935,
'sex': -0.0846541637961565,
'married': 0.07451889604357693,
'educ': -0.06098723778992694,
'illness': 0.039430607922053884,
'injury': -0.002779341753010467,
'illdays': -0.006551280792122459,
'actdays': 0.14663287713359757}

The coefficient values are about the same. This means that the penalization made little difference with this dataset.

Conclusion

Ridge regression allows you to penalize variables based on their useful in developing the model. With this form of regularized regression the coefficients of the variables is never set to zero. Other forms of regularization regression allows for the total removal of variables. One example of this is lasso regression.

Computational Thinking

Computational thinking is the process of expressing a problem in a way that a computer can solve. In general, there are four various ways that computational thinking can be done. These four ways are decomposition, pattern recognition, abstraction, and algorithmic thinking.

Although computational thinking is dealt with in the realm of computer science. Everyone thinks computationally at one time or another especially in school. Awareness of these subconscious strategies can help people to know how they think at times as well as to be aware of the various ways in which thinking is possible.

Decomposition

Decomposition is the process of breaking a large problem down into smaller and smaller parts or problems. The benefit of this is that by addressing all of the created little problems you can solve the large problem.

In education decomposition can show up in many ways. For teachers, they often have to break goals done into objectives, and sometimes down into procedures in a daily lesson plan. Seeing the big picture of the content students need and breaking it down into pieces that students can comprehend is critically to education such as with chunking.

For the student, decomposition involves breaking down the parts of a project such as writing a paper. The student has to determine what to do and how it helps to achieve the completion of their project.

Pattern Recognition

Pattern recognition has to refer to how various aspects of a problem have things in common. For a teacher, this may involve the development of a thematic unit. Developing such a unit requires the teacher to see what various subjects or disciplines have in common as they try to create the thematic unit.

For the student, pattern recognition can support the development of  shortcuts. Examples include seeing similarities in assignments that need to be completed and completing similar assignments together.

Abstraction

Abstraction  is the ability to remove irrelevant information from a problem. This is perhaps the most challenging form of thinking to develop because people often fall into the trap that everything is important.

For a teacher, abstractions involves teaching only the critical information that is in the content and not stressing the small stuff. This is not easy especially when the  teacher has a passion for their subject. This often blinds them to trying to share only the most relevant information about their field with their students.

For students, abstraction involves being able to share the most critical information. Students are guilty of the same problems as teachers in that they share everything when writing or presenting. Determining what is important requires the development of an opinion to judge the relevance of something. This is a skill that is hard to find among graduates.

Algorithmic Thinking

Algorithmic thinking is being able to develop a step-by-step plan to do something. For teachers, this happens everyday through planning activities and leading a class. Planning may be the most common form of thinking for the teacher.

For students, algorithmic thinking is somewhat more challenging. It is common for younger people to rely heavily on intuition to accomplish tasks. This means that they did something but they do not know how they did it.

Another common mistake for young people is doing things through brute force. Rather than planning, they will just keep pounding away until something works. As such, it is common for students to do things the “hard way” as the saying goes.

Conclusion

Computational thinking is really how humans think in many situations in which emotions are not the primary mover. As such, what is really happening is not that computers are thinking as much as they are trying to model how humans think. In education, there are several situations. In which computational thinking can be employed for success.

3 Steps to Successful Research

When students have to conduct a research project they often struggle with determining what to do. There are many decisions that have to be made that can impede a student’s chances of achieving success.  However, there are ways to overcome this problem.

This post will essentially reduce the decision-making process for conducting research down to three main questions that need to be addressed. These questions are.

  • What do you Want to Know?
  • How do You Get the Answer?
  • What Does Your Answer Mean?

Answering these three questions makes it much easier to develop a sense of direction and scope in order to complete a project.

What do you Want to Know?

Often, students want to complete a project but it is unclear to them what they are trying to figure out. In other words, the students do not know what it is that they want to know. Therefore, one of the first steps in research is to determine exactly it is you want to know.

Understanding what you want to know will allow you to develop a problem as well as research questions to facilitate your ability to understand exactly what it is that you are looking for. Research always begins with a problem and questions about the problem and this is simply another way of stating what it is that you want to know.

How do You Get the Answer?

Once it is clear what it is that you want to know it is critical that you develop a process for determining how you will obtain the answers. It is often difficult for students to develop a systematic way in which to answer questions. However, in a research paradigm, a scientific way of addressing questions is critical.

When you are determining how to get answers to what you want to know this is essential the development of your methodology section. This section includes such matters as the research design, sample, ethics, data analysis, etc. The purpose here is again to explain the way to get the answer(s).

What Does Your Answer Mean?

After you actually get the answer you have to explain what it means. Many students fall into the trap of doing something without understanding why or determining the relevance of the outcome. However, a research project requires some sort of interpretation or explanation of the results. Just getting the answer is not enough it is the meaning that holds the power.

Often, the answers to the research questions are found in the results section of a paper and the meaning is found in the discussion and conclusion section. In the discussion section, you explain the major findings with interpretation, sare recommendations, and provide a conclusion. This requires thought into the usefulness of what you wanted to know. In other words, you are explaining why someone else should care about your work. This is much harder to do than many realize.

Conclusion

Research is challenging but if you keep in mind these three keys it will help you to see the big picture of research and o focus on the goals of your study and not so much on the tiny details that encompasses the processes.

Random Forest Regression in Python

Random forest is simply the making of dozens if not thousands of decision trees. The decision each tree makes about an example are then tallied for the purpose of voting with the classification that receives the most votes winning. For regression, the results of the trees are averaged in  order to give the most accurate results

In this post, we will use the cancer dataset from the pydataset module to predict the age of people. Below is some initial code.

import pandas as pd
import numpy as np
from pydataset import data
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

We can load our dataset as df, drop all NAs, and create our dataset that contains the independent variables and a separate dataset that includes the dependent variable of age. The code is below

df = data('cancer')
df=df.dropna()
X=df[['time','status',"sex","ph.ecog",'ph.karno','pat.karno','meal.cal','wt.loss']]
y=df['age']

Next, we need to set up our train and test sets using a 70/30 split. After that, we set up our model using the RandomForestRegressor function. n_estimators is the number of trees we want to create and the random_state argument is for supporting reproducibility. The code is below

x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
h=RandomForestRegressor(n_estimators=100,random_state=1)

We can now run our model and test it. Running the model requires the .fit() function and testing involves the .predict() function. The results of the test are found using the mean_squared_error() function.

h.fit(x_train,y_train)
y_pred=h.predict(x_test)
mean_squared_error(y_test,y_pred)
71.75780196078432

The MSE of 71.75 is only useful for model comparison and has little meaning by its self. Another way to assess the model is by determining variable importance. This helps you to determine in a descriptive way the strongest variables for the regression model. The code is below followed by the plot of the variables.

model_ranks=pd.Series(h.feature_importances_,index=x_train.columns,name="Importance").sort_values(ascending=True,inplace=False) 
ax=model_ranks.plot(kind='barh')

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As you can see, the strongest predictors of age include calories per meal, weight loss, and time sick. Sex and whether the person is censored or dead make a smaller difference. This makes sense as younger people eat more and probably lose more weight because they are heavier initially when dealing with cancer.

Conclusison

This post provided an example of the use of regression with random forest. Through the use of ensemble voting, you can improve the accuracy of your models. This is a distinct power that is not available with other machine learning algorithm.

Support Vector Machines Regression with Python

This post will provide an example of how to do regression with support vector machines SVM. SVM is a complex algorithm that allows for the development of non-linear models. This is particularly useful for messy data that does not have clear boundaries.

The steps that we will use are listed below

  1. Data preparation
  2. Model Development

We will use two different kernels in our analysis. The LinearSVR kernel and SVR kernel. The difference between these two kernels has to do with slight changes in the calculations of the boundaries between classes.

Data Preparation

We are going to use the OFP dataset available in the pydataset module. This dataset was used previously for classification with SVM on this site. Our plan this time is that we want to predict family inc (famlinc), which is a continuous variable.  Below is some initial code.

import numpy as np
import pandas as pd
from pydataset import data
from sklearn import svm
from sklearn import model_selection
from statsmodels.tools.eval_measures import mse

We now need to load our dataset and remove any missing values.

df=pd.DataFrame(data('OFP'))
df=df.dropna()

AS in the previous post, we need to change the text variables into dummy variables and we also need to scale the data. The code below creates the dummy variables, removes variables that are not needed, and also scales the data.

dummy=pd.get_dummies(df['black'])
df=pd.concat([df,dummy],axis=1)
df=df.rename(index=str, columns={"yes": "black_person"})
df=df.drop('no', axis=1)

dummy=pd.get_dummies(df['sex'])
df=pd.concat([df,dummy],axis=1)
df=df.rename(index=str, columns={"male": "Male"})
df=df.drop('female', axis=1)

dummy=pd.get_dummies(df['employed'])
df=pd.concat([df,dummy],axis=1)
df=df.rename(index=str, columns={"yes": "job"})
df=df.drop('no', axis=1)

dummy=pd.get_dummies(df['maried'])
df=pd.concat([df,dummy],axis=1)
df=df.rename(index=str, columns={"no": "single"})
df=df.drop('yes', axis=1)

dummy=pd.get_dummies(df['privins'])
df=pd.concat([df,dummy],axis=1)
df=df.rename(index=str, columns={"yes": "insured"})
df=df.drop('no', axis=1)
df=df.drop(['black','sex','maried','employed','privins','medicaid','region','hlth'],axis=1)
df = (df - df.min()) / (df.max() - df.min())
df.head()

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We now need to set up our datasets. The X dataset will contain the independent variables while the y dataset will contain the dependent variable

X=df[['ofp','ofnp','opp','opnp','emr','hosp','numchron','adldiff','age','school','single','black_person','Male','job','insured']]
y=df['faminc']

We can now move to model development

Model Development

We now need to create our train and test sets for or X and y datasets. We will do a 70/30 split of the data. Below is the code

X_train,X_test,y_train,y_test=model_selection.train_test_split(X,y,test_size=.3,random_state=1)

Next, we will create our two models with the code below.

h1=svm.SVR()
h2=svm.LinearSVR()

We will now run our first model and assess the results. Our metric is the mean squared error. Generally, the lower the number the better.  We will use the .fit() function to train the model and the .predict() function for test the model

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The mse was 0.27. This number means nothing only and is only beneficial for comparison reasons. Therefore, the second model will be judged as better or worst only if the mse is lower than 0.27. Below are the results of the second model.

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We can see that the mse for our second model is 0.34 which is greater than the mse for the first model. This indicates that the first model is superior based on the current results and parameter settings.

Conclusion

This post provided an example of how to use SVM for regression.

Analyzing Twitter Data in Python

In this post, we will look at how to analyze text from Twitter. We will do each of the following for tweets that refer to Donald Trump and tweets that refer to Barrack Obama.

  • Conduct a sentiment analysis
  • Create a word cloud

This is a somewhat complex analysis so I am assuming that you are familiar with Python as explaining everything would make the post much too long. In order to achieve our two objectives above we need to do the following.

  1. Obtain all of the necessary information from your twitter apps account
  2. Download the tweets & clean
  3. Perform the analysis

Before we begin, here is a list of modules we will need to load to complete our analysis

import wordcloud
import matplotlib.pyplot as plt
import twython
import re
import numpy

Obtain all Needed Information

From your twitter app account, you need the following information

  • App key
  • App key secret
  • Access token
  • Access token secret

All this information needs to be stored in individual objects in Python. Then each individual object needs to be combined into one object. The code is below.

TWITTER_APP_KEY=XXXXXXXXXXXXXXXXXXXXXXXXXX
TWITTER_APP_KEY_SECRET=XXXXXXXXXXXXXXXXXXX
TWITTER_ACCESS_TOKEN=XXXXXXXXXXXXXXXXXXXXX
TWITTER_ACCESS_TOKEN_SECRET=XXXXXXXXXXXXXX
t=twython.Twython(app_key=TWITTER_APP_KEY,app_secret=TWITTER_APP_KEY_SECRET,oauth_token=TWITTER_ACCESS_TOKEN,oauth_token_secret=TWITTER_ACCESS_TOKEN_SECRET)

In the code above we saved all the information in different objects at first and then combined them. You will of course replace the XXXXXXX with your own information.

Next, we need to create a function that will pull the tweets from Twitter. Below is the code,

def get_tweets(twython_object,query,n):
   count=0
   result_generator=twython_object.cursor(twython_object.search,q=query)

   result_set=[]
   for r in result_generator:
      result_set.append(r['text'])
      count+=1
      if count ==n: break

   return result_set

You will have to figure out the code yourself. We can now download the tweets.

Downloading Tweets & Clean

Downloading the tweets involves making an empty dictionary that we can save our information in. We need two keys in our dictionary one for Trump and the other for Obama because we are downloading tweets about these two people.

There are also two additional things we need to do. We need to use regular expressions to get rid of punctuation and we also need to lower case all words. All this is done in the code below.

tweets={}
tweets['trump']=[re.sub(r'[-.#/?!.":;()\']',' ',tweet.lower())for tweet in get_tweets(t,'#trump',1500)]
tweets['obama']=[re.sub(r'[-.#/?!.":;()\']',' ',tweet.lower())for tweet in get_tweets(t,'#obama',1500)]

The get_tweets function is also used in the code above along with our twitter app information. We pulled 1500 tweets concerning Obama and 1500 tweets about Trump. We were able to download and clean our tweets at the same time. We can now do our analysis

Analysis

To do the sentiment analysis you need dictionaries of positive and negative words. The ones in this post were taken from GitHub. Below is the code for loading them into Python.

positive_words=open('XXXXXXXXXXXX').read().split('\n')
negative_words=open('XXXXXXXXXXXX').read().split('\n')

We now will make a function to calculate the sentiment

def sentiment_score(text,pos_list,neg_list):
   positive_score=0
   negative_score=0

   for w in text.split(' '):
      if w in pos_list:positive_score+=1
      if w in neg_list:negative_score+=1
   return positive_score-negative_score

Now we create an empty dictionary and run the analysis for Trump and then for Obama

tweets_sentiment={}
tweets_sentiment['trump']=[sentiment_score(tweet,positive_words,negative_words)for tweet in tweets['trump']]
tweets_sentiment['obama']=[sentiment_score(tweet,positive_words,negative_words)for tweet in tweets['obama']]

Now we can make visuals of our results with the code below

trump=plt.hist(tweets_sentiment['trump'],5)
obama=plt.hist(tweets_sentiment['obama'],5)

Obama is on the left and trump is on the right. It seems that trump tweets are consistently more positive. Below are the means for both.

numpy.mean(tweets_sentiment['trump'])
Out[133]: 0.36363636363636365

numpy.mean(tweets_sentiment['obama'])
Out[134]: 0.2222222222222222

Trump tweets are slightly more positive than Obama tweets. Below is the code for the Trump word cloud

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Here is the code for the Obama word cloud

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A lot of speculating can be made from the word clouds and sentiment analysis. However, the results will change every single time because of the dynamic nature of Twitter. People are always posting tweets which changes the results.

Conclusion

This post provided an example of how to download and analyze tweets from twitter. It is important to develop a clear idea of what you want to know before attempting this sort of analysis as it is easy to become confused and not accomplish anything.

Multiple Regression in Python

In this post, we will go through the process of setting up and a regression model with a training and testing set using Python. We will use the insurance dataset from kaggle. Our goal will be to predict charges. In this analysis, the following steps will be performed.

  1. Data preparation
  2. Model training
  3. model testing

Data Preparation

Below is a list of the modules we will need in order to complete the analysis

import matplotlib.pyplot as plt
import pandas as pd
from sklearn import linear_model,model_selection, feature_selection,preprocessing
import statsmodels.formula.api as sm
from statsmodels.tools.eval_measures import mse
from statsmodels.tools.tools import add_constant
from sklearn.metrics import mean_squared_error

After you download the dataset you need to load it and take a look at it. You will use the  .read_csv function from pandas to load the data and .head() function to look at the data. Below is the code and the output.

insure=pd.read_csv('YOUR LOCATION HERE')

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We need to create some dummy variables for sex, smoker, and region. We will address that in a moment, right now we will look at descriptive stats for our continuous variables. We will use the .describe() function for descriptive stats and the .corr() function to find the correlations.

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The descriptives are left for your own interpretation. As for the correlations, they are generally weak which is an indication that regression may be appropriate.

As mentioned earlier, we need to make dummy variables sex, smoker, and region in order to do the regression analysis. To complete this we need to do the following.

  1. Use the pd.get_dummies function from pandas to create the dummy
  2. Save the dummy variable in an object called ‘dummy’
  3. Use the pd.concat function to add our new dummy variable to our ‘insure’ dataset
  4. Repeat this three times

Below is the code for doing this

dummy=pd.get_dummies(insure['sex'])
insure=pd.concat([insure,dummy],axis=1)
dummy=pd.get_dummies(insure['smoker'])
insure=pd.concat([insure,dummy],axis=1)
dummy=pd.get_dummies(insure['region'])
insure=pd.concat([insure,dummy],axis=1)
insure.head()

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The .get_dummies function requires the name of the dataframe and in the brackets the name of the variable to convert. The .concat function requires the name of the two datasets to combine as well the axis on which to perform it.

We now need to remove the original text variables from the dataset. In addition, we need to remove the y variable “charges” because this is the dependent variable.

y = insure.charges
insure=insure.drop(['sex', 'smoker','region','charges'], axis=1)

We can now move to model development.

Model Training

Are train and test sets are model with the model_selection.trainin_test_split function. We will do an 80-20 split of the data. Below is the code.

X_train, X_test, y_train, y_test = model_selection.train_test_split(insure, y, test_size=0.2)

In this single line of code, we create a train and test set of our independent variables and our dependent variable.

We can not run our regression analysis. This requires the use of the .OLS function from statsmodels module. Below is the code.

answer=sm.OLS(y_train, add_constant(X_train)).fit()

In the code above inside the parentheses, we put the dependent variable(y_train) and the independent variables (X_train). However, we had to use the function add_constant to get the intercept for the output. All of this information is then used inside the .fit() function to fit a model.

To see the output you need to use the .summary() function as shown below.

answer.summary()

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The assumption is that you know regression but our reading this post to learn python. Therefore, we will not go into great detail about the results. The r-square is strong, however, the region and gender are not statistically significant.

We will now move to model testing

Model Testing

Our goal here is to take the model that we developed and see how it does on other data. First, we need to predict values with the model we made with the new data. This is shown in the code below

ypred=answer.predict(add_constant(X_test))

We use the .predict() function for this action and we use the X_test data as well. With this information, we will calculate the mean squared error. This metric is useful for comparing models. We only made one model so it is not that useful in this situation. Below is the code and results.

print(mse(ypred,y_test))
33678660.23480476

For our final trick, we will make a scatterplot with the predicted and actual values of the test set. In addition, we will calculate the correlation of the predict values and test set values. This is an alternative metric for assessing a model.

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You can see the first two lines are for making the plot. Lines 3-4 are for making the correlation matrix and involves the .concat() function. The correlation is high at 0.86 which indicates the model is good at accurately predicting the values. THis is confirmed with the scatterplot which is almost a straight line.

Conclusion

IN this post we learned how to do a regression analysis in Python. We prepared the data, developed a model, and tested a model with an evaluation of it.

Logistic Regression in Python

This post will provide an example of a logistic regression analysis in Python. Logistic regression is commonly used when the dependent variable is categorical. Our goal will be to predict the gender of an example based on the other variables in the model. Below are the steps we will take to achieve this.

  1. Data preparation
  2. Model development
  3. Model testing
  4. Model evaluation

Data Preparation

The dataset we will use is the ‘Survey of Labour and Income Dynamics’ (SLID) dataset available in the pydataset module in Python. This dataset contains basic data on labor and income along with some demographic information. The initial code that we need is below.

import pandas as pd
import statsmodels.api as sm
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn import metrics
from pydataset import data

The code above loads all the modules and other tools we will need in this example. We now can load our data. In addition to loading the data, we will also look at the count and the characteristics of the variables. Below is the code.

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At the top of this code, we create the ‘df’ object which contains our data from the “SLID”. Next, we used the .count() function to determine if there was any missing data and to see what variables were available. It appears that we have five variables and a lot of missing data as each variable has different amounts of data. Lastly, we used the .head() function to see what each variable contained. It appears that wages, education, and age are continuous variables well sex and language are categorical. The categorical variables will need to be converted to dummy variables as well.

The next thing we need to do is drop all the rows that are missing data since it is hard to create a model when data is missing. Below is the code and the output for this process.

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In the code above, we used the .dropna() function to remove missing data. Then we used the .count() function to see how many rows remained. You can see that all the variables have the same number of rows which is important for model analysis. We will now make our dummy variables for sex and language in the code below.

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Here is what we did,

  1. We used the .get_dummies function from pandas first on the sex variable. All this was stored in a new object called “dummy”
  2. We then combined the dummy and df datasets using the .concat() function. The axis =1 argument is for combing by column.
  3. We repeat steps 1 and 2 for the language variable
  4. Lastly, we used the .head() function to see the results

With this, we are ready to move to model development.

Model Development

The first thing we need to do is put all of the independent variables in one dataframe and the dependent variable in its own dataframe. Below is the code for this

X=df[['wages','education','age',"French","Other"]]
y=df['Male']

Notice that we did not use every variable that was available. For the language variables, we only used “French” and “Other”. This is because when you make dummy variables you only need k-1 dummies created. Since the language variable had three categories we only need two dummy variables. Therefore, we excluded “English” because when “French” and “Other” are coded 0 it means that “English” is the characteristic of the example.

In addition, we only took “male” as our dependent variable because if “male” is set to 0 it means that example is female. We now need to create our train and test dataset. The code is below.

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

We created four datasets

  • train dataset with the independent variables
  • train dataset with the dependent variable
  • test dataset with the independent variables
  • test dataset with the independent variable

The split is 70/30 with 70% being used for the training and 30% being used for testing. This is the purpose of the “test_size” argument. we used the train_test_split function to do this. We can now run our model and get the results. Below is the code.

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Here is what we did

  1. We used the .Logit() function from statsmodel to create the logistic model. Notice we used only the training data.
  2. We then use the .fit() function to get the results and stored this in the result object.
  3. Lastly, we printed the results in the ‘result’ object using the .summary()

There are some problems with the results. The Pseudo R-square is infinity which is usually. Also, you may have some error output about hessian inversion in your output. For these reasons, we cannot trust the results but will continue for the sake of learning.

The coefficients are clear.  Only wage, education, and age are significant. In order to determine the probability you have to take the coefficient from the model and use the .exp() function from numpy. Below are the results.

np.exp(.08)
Out[107]: 1.0832870676749586

np.exp(-0.06)
Out[108]: 0.9417645335842487

np.exp(-.01)
Out[109]: 0.9900498337491681

For the first value, for every unit wages increaser the probability that they are male increase 8%. For every 1 unit increase in education there probability of the person being male decrease 6%. Lastly, for every one unit increase in age the probability of the person being male decrease by 1%. Notice that we subtract 1 from the outputs to find the actual probability.

We will now move to model testing

Model Testing

To do this we first test our model with the code below

y_pred=result.predict(X_test)

We made the result object earlier. Now we just use the .predict() function with the X_test data. Next, we need to flag examples that the model believes has a 60% chance or greater of being male. The code is below

y_pred_flag=y_pred>.6

This creates a boolean object with True and False as the output. Now we will make our confusion matrix as well as other metrics for classification.

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The results speak for themselves. There are a lot of false positives if you look at the confusion matrix. In addition precision, recall, and f1 are all low. Hopefully, the coding should be clear the main point is to be sure to use the test set dependent dataset (y_test) with the flag data you made in the previous step.

We will not make the ROC curve. For a strong model, it should have a strong elbow shape while with a weak model it will be a diagonal straight line.

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The first plot is of our data. The second plot is what a really bad model would look like. As you can see there is littte difference between the two. Again this is because of all the false positives we have in the model. The actual coding should be clear. fpr is the false positive rate, tpr is the true positive rate. The function is .roc_curve. Inside goes the predict vs actual test data.

Conclusion

This post provided a demonstration of the use of logistic regression in Python. It is necessary to follow the steps above but keep in mind that this was a demonstration and the results are dubious.

Prerequistes to Conducting Research

Some of the biggest challenges in helping students with research is their lack of preparation. The problem is not an ignorance of statistics or research design as that takes only a little bit of support. The real problem is that students want to do research without hardly reading any research and lacking knowledge of how research writing is communicated. This post will share some prerequisites to performing research.

Read Extensively

Extensive reading means reading broadly about a topic and not focusing too much on specifics. Therefore, you read indiscriminately perhaps limited yourself only to your general discipline.

In order to communicate research, you must first be familiar with the vocabulary and norms of research. This can be learned to a great extent through reading academic empirical articles.

The ananoloy I like to use is how a baby learns. By spends large amounts of time being exposed to the words and actions of others. The baby has no real idea in terms of what is going on at first. However, after continuous exposure, the child begins to understand the words and actions fo those around them and even begins to mimic the behaviors.

In many ways, this is the purpose of reading a great deal before even attempting to do any research. Just as the baby, a writer needs to observe how others do things, continue this process even if they do not understand, and attempt to imitate the desired behaviors. You must understand the forms of communication as well as the cultural expectations of research writing and this can only happen through direct observation.

At the end of this experience, you begin to notice a pattern in terms of the structure of research writing. The style is highly ridge with litter variation.

It is hard to say how much extensive reading a person needs. Generally, the more reading that was done in the past the less reading needed to understand the structure of research writing. If you hate to read and did little reading in the past you will need to read a lot more to understand research writing then someone with an extensive background in reading. In addition, if you are trying to write in a second language you will need to read much more than someone writing in their native language.

If you are still desirous of a hard number of articles to read I would say aim for the following

  • Native who loves to read-at least 25 articles
  • Native who hates to read-at least 40 articles
  • Non-native reader-60 articles or more

Extensive reading is just reading. There is no notetaking or even highlighting. You are focusing on exposure only. Just as the observant baby so you are living in the moment trying to determine what is the appropriate behavior. If you don’t understand you need to keep going anyway as the purpose is quantity and not quality. Generally, when the structure of the writing begins to become redundant ad you can tell what the author is doing without having to read too closely you are ready to move on.

Read Intensively

Intensive reading is reading more for understanding. This involves slows with the goal of deeper understanding. Now you select something, in particular, you want to know. Perhaps you want to become more familiar with the writing of one excellent author or maybe there is one topic in particular that you are interested in. With intensive writing, you want to know everything that is happening in the text. To achieve this you read fewer articles and focus much more on quality over quantity.

By the end of the extensive and intensive reading, you should be familiar with the following.

  • The basic structure of research writing even if you don’t understand why it is the way it is.
  • A more thorough understanding of something specific you read about during your intensive reading.
  • Some sense of purpose in terms of what you need to do for your own writing.
  • A richer vocabulary and content knowledge related to your field.

Write Academicly

Once a student has read a lot of research there is some hope that they can now attempt to write in this style. As the teacher, it is my responsibility to point out the structure of research writing which involves such as ideas as the 5 sections and the parts of each section.

Students grasp this but they often cannot build paragraphs. In order to write academic research, you must know the purpose of main ideas, supporting details, and writing patterns. If these terms are unknown to you it will be difficult to write research that is communicated clearly.

The main idea is almost always the first sentence of a paragraph and writing patterns provide different ways to organize the supporting details. This involves understanding the purpose of each paragraph that is written which is a task that many students could not explain. This is looking at writing from a communicative or discourse perspective and not at a minute detail or grammar one.

The only way to do this is to practice writing. I often will have students develop several different reviews of literature. During this experience, they learn how to share the ideas of others. The next step is developing a proposal in which the student shares their ideas and someone else’s. The final step is writing a formal research paper.

Conclusion

To write you must first observe how others write. Then you need to imitate what you saw. Once you can do it what others have done it will allow you to ask questions about why things are this way. Too often, people just want to write without even understanding what they are trying to do. This leads to paralysis at best (I don’t know what to do) to a disaster at worst (spending hours confidently writing garbage). The enemy to research is not methodology as many people write a lot without knowledge of stats or research design because they collaborate. The real enemy of research is neglecting the preparation of reading and the practicing of writing.

Writing Discussion & Conclusions in Research

The Discussion & Conclusion section of a research article/thesis/dissertation is probably the trickiest part of a project to write. Unlike the other parts of a paper, the Discussion & Conclusions are hard to plan in advance as it depends on the results. In addition, since this is the end of a paper the writer is often excited and wants to finish it quickly, which can lead to superficial analysis.

This post will discuss common components of the Discussion & Conclusion section of a paper. Not all disciplines have all of these components nor do they use the same terms as the ones mentioned below.

Discussion

The discussion is often a summary of the findings of a paper. For a thesis/dissertation, you would provide the purpose of the study again but you probably would not need to share this in a short article. In addition, you also provide highlights of what you learn with interpretation. In the results section of a paper, you simply state the statistical results. In the discussion section, you can now explain what those results mean for the average person.

The ordering of the summary matters as well. Some recommend that you go from the most important finding to the least important. Personally, I prefer to share the findings by the order in which the research questions are presented. This maintains a cohesiveness across sections of a paper that a reader can appreciate. However, there is nothing superior to either approach. Just remember to connect the findings with the purpose of the study as this helps to connect the themes of the paper together.

What really makes this a discussion is to compare/contrast your results with the results of other studies and to explain why the results are similar and or different. You also can consider how your results extend the works of other writers. This takes a great deal of critical thinking and familiarity with the relevant literature.

Recommendation/Implications

The next component of this final section of the paper is either recommendations or implications but almost never both. Recommendations are practical ways to apply the results of this study through action. For example, if your study finds that sleeping 8 hours a night improves test scores then the recommendation would be that students should sleep 8 hours a night to improve their test scores. This is not an amazing insight but the recommendations must be grounded in the results and not just opinion.

Implications, on the other hand, explain why the results are important. Implications are often more theoretical in nature and lack the application of recommendations. Often implications are used when it is not possible to provide a strong recommendation.

The terms conclusion and implications are often used interchangeably in different disciplines and this is highly confusing. Therefore, keep in mind your own academic background when considering what these terms mean.

There is one type of recommendation that is almost always present in a study and that is recommendations for further study. This is self-explanatory but recommendations for further study are especially important if the results are preliminary in nature. A common way to recommend further studies is to deal with inconclusive results in the current study. In other words, if something weird happened in your current paper or if something surprised you this could be studied in the future. Another term for this is “suggestions for further research.”

Limitations

Limitations involve discussing some of the weaknesses of your paper. There is always some sort of weakness with a sampling method, statistical analysis, measurement, data collection etc. This section is an opportunity to confess these problems in a transparent matter that further researchers may want to control for.

Conclusion

Finally, the conclusion of the Discussion & Conclusion is where you try to summarize the results in a sentence or two and connect them with the purpose of the study. In other words, trying to shrink the study down to a one-liner. If this sounds repetitive it is and often the conclusion just repeats parts of the discussion.

Blog Conclusion

This post provides an overview of writing the final section of a research paper. The explanation here provides just one view on how to do this. Every discipline and every researcher has there own view on how to construct this section of a paper.

Shaping the Results of a Research Paper

Writing the results of a research paper is difficult. As a researcher, you have to try and figure out if you answered the question. In addition, you have to figure out what information is important enough to share. As such it is easy to get stuck at this stage of the research experience. Below are some ideas to help with speeding up this process.

Consider the Order of the Answers

This may seem obvious but probably the best advice I could give a student when writing their results section is to be sure to answer their questions in the order they presented them in the introduction of their study. This helps with cohesion and coherency. The reader is anticipating answers to these questions and they often subconsciously remember the order the questions came in.

If a student answers the questions out of order it can be jarring for the reader. When this happens the reader starts to double check what the questions were and they begin to second-guess their understanding of the paper which reflects poorly on the writer. An analogy would be that if you introduce three of your friends to your parents you might share each person’s name and then you might go back and share a little bit of personal information about each friend. When we do this we often go in order 1st 2nd 3rd friend and then going back and talking about the 1st friend. The same courtesy should apply when answering research questions in the results section. Whoever was first is shared first etc.

Consider how to Represent the Answers

Another aspect to consider is the presentation of the answers. Should everything be in text? What about the use of visuals and tables?  The answers depend on several factors

  • If you have a small amount of information to share writing in paragraphs is practical. Defining small depends on how much space you have to write as well but generally anything more than five ideas should be placed in a table and referred too.
  • Tables are for sharing large amounts of information. If an answer to a research question requires more than five different pieces of information a table may be best. You can extract really useful information from a table and place it directly in paragraphs while referring the reader to the table for even more information.
  • Visuals such as graphs and plots are not used as frequently in research papers as I would have thought. This may be because they take up so much space in articles that usually have page limits. In addition, readers of an academic journal are pretty good at visually results mentally based on numbers that can be placed in a table. Therefore, visuals are most appropriate for presentations and writing situations in which there are fewer constraints on the length of the document such as a thesis or dissertation.

Know when to Interpret

Sometimes I have had students try to explain the results while presenting them. I cannot say this is wrong, however, it can be confusing. The reason it is so confusing is that the student is trying to do two things at the same time which are present the results and interpret them. This would be ok in a presentation and even expected but when someone is reading a paper it is difficult to keep two separate threads of thought going at the same time.  Therefore, the meaning or interpretation of the results should be saved for the Discussion Conclusion section.

Conclusion

Presenting the results is in many ways the high point of a research experience. It is not easy to take numerical results and try to capture the useful information clearly. As such, the advice given here is intending to help support this experience

Purpose of a Quantitative Methodology

Students often struggle with shaping their methodology section in their paper. The problem is often that students do not see the connection between the different sections of a research paper. This inability to connect the dots leads to isolated thinking on the topic and inability to move forward.

The methodology section of a research paper plays a critical role. In brief, the purpose of a methodology is to explain to your readers how you will answer your research questions. In the strictest sense, this is important for reproducing a study. Therefore, what is really important when writing a methodology is the research questions of the study. The research questions determine the following of a methodology.

What this means is that a student must know what they want to know in order to explain how they will find the answers. Below is a description of these sections along with one section that is not often influenced by the research questions.

Sample & Setting

In the sample section of the methodology, it is common or the student to explain the setting of the study, provide some demographics, and explain the sampling method. In this section of the methodology, the goal is to describe what the reader needs to know about the participants in order to understand the context from which the results were derived.

Research Design & Scales

The research design explains specifically how the data was collected. There are several standard ways to do this in the social sciences such.

  • Survey design
  • experimental design
  • correlational design

Within this section, some academic disciplines also explain the scales or the tool used to measure the variable(s) of the study. Again, it is impossible to develop this section of the research questions are unclear or unknown.

Data Analysis

The data analysis section provides an explanation of how the answers were calculated in a study. Success in this section requires a knowledge of the various statistical tools that are available. However, understanding the research questions is key to articulating this section clearly.

Ethics

A final section in many methodologies is ethics. The ethical section is a place where the student can explain how the protected participant’s anonymity, made sure to get the permission and other aspects of morals. This section is required by most universities in order to gain permission to do research. However, it is often missing from journals.

Conclusion

The methodology is part of the larger picture of communicating one’s research. It is important that a research paper is not seen as isolated parts but rather as a whole. The reason for this position is that a paper cannot make sense on its own if any of these aspects are missing.

Tips for Writing a Quantitative Review of Literature

Writing a review of literature can be challenging for students. The purpose here is to try and synthesize a huge amount of information and to try and communicate it clearly to someone who has not read what you have read.

From my experience working with students, I have developed several tips that help them to make faster decisions and to develop their writing as well.

Remember the  Purpose

Often a student will collect as many articles as possible and try to throw them all together to make a review of the literature. This naturally leads to problems of the paper sounded like a shopping list of various articles. Neither interesting nor coherent.

Instead, when writing a review of literature a student should keep in mind the question

What do my readers need to know in order to understand my study?

This is a foundational principle when writing. Readers don’t need to know everything only what they need to know to appreciate the study they are ready. An extension of this is that different readers need to know different things. As such, there is always a contextual element to framing a review of the literature.

Consider the Format

When working with a student, I always recommend the following format to get there writing started.

For each major variable in your study do the following…

  1. Define it
  2. Provide examples or explain theories about it
  3. Go through relevant studies thematically

Definition

There first thing that needs to be done is to provide a definition of the construct. This is important because many constructs are defined many different ways. This can lead to confusion if the reader is thinking one definition and the writer is thinking another.

Examples and Theories

Step 2 is more complex. After a definition is provided the student can either provide an example of what this looks like in the real world and or provide more information in regards to theories related to the construct.

Sometimes examples are useful. For example, if writing a paper on addiction it would be useful to not only define it but also to provide examples of the symptoms of addiction. The examples help the reader to see what used to be an abstract definition in the real world.

Theories are important for providing a deeper explanation of a construct. Theories tend to be highly abstract and often do not help a reader to understand the construct better. One benefit of theories is that they provide a historical background of where the construct came from and can be used to develop the significance of the study as the student tries to find some sort of gap to explore in their own paper.

Often it can be beneficial to include both examples and theories as this demonstrates stronger expertise in the subject matter. In theses and dissertations, both are expected whenever possible. However, for articles space limitations and knowing the audience affects the inclusion of both.

Relevant Studies

The relevant studies section is similar breaking news on CNN. The relevant studies should generally be newer. In the social sciences, we are often encouraged to look at literature from the last five years, perhaps ten years in some cases. Generally, readers want to know what has happened recently as experience experts are familiar with older papers. This rule does not apply as strictly to theses and dissertations.

Once recent literature has been found the student needs to organize it thematically. The reason for a thematic organization is that the theme serves as the main idea of the section and the studies themselves serve as the supporting details. This structure is surprisingly clear for many readers as the predictable nature allows the reader to focus on content rather than on trying to figure out what the author is tiring to say. Below is an example

There are several challenges with using technology in class(ref, 2003; ref 2010). For example, Doe (2009) found that technology can be unpredictable in the classroom. James (2010) found that like of training can lead some teachers to resent having to use new technology

The main idea here is “challenges with technology.” The supporting details are Doe (2009) and James (2010). This concept of themes is much more complex than this and can include several paragraphs and or pages.

Conclusion

This process really cuts down on the confusion of students writing. For stronger students, they can be free to do what they want. However, many students require structure and guidance when the first begin writing research papers

Common Problems with Research for Students

I have worked with supporting undergrad and graduate students with research projects for several years. This post is what I consider to be the top reasons why students and even the occasional faculty member struggles to conduct research. The reasons are as follows

  1. They don’t read
  2. No clue what  a problem is
  3. No questions
  4. No clue how to measure
  5. No clue how to analyze
  6. No clue how to report

Lack of Reading

The first obstacle to conducting research is that students frequently do not read enough to conceptualize how research is done. Reading not just anything bust specifically research allows a student to synthesize the vocabulary and format of research writing. You cannot do research unless you first read research. This axiom applies to all genres of writing.

A common complaint is the difficulty with understanding research articles. For whatever reason, the academic community has chosen to write research articles in an exceedingly dense and unclear manner. This is not going to change because one graduate student cannot understand what the experts are saying. Therefore, the only solution to understand research English is exposure to this form of communication.

Determining the Problem

If a student actually reads they often go to the extreme of trying to conduct Nobel Prize type research. In other words, their expectations are overinflated given what they know. What this means is that the problem they want to study is infeasible given the skillset they currently possess.

The opposite extreme is to find such a minute problem that nobody cares about it. Again, reading will help in avoiding this two pitfalls.

Another problem is not knowing exactly how to articulate a problem. A student will come to me with excellent examples of a problem but they never abstract or take a step away from the examples of the problem to develop a researchable problem. There can be no progress without a clearly defined research problem.

Lack the Ability to Ask Questions about the Problem

If a student actually has a problem they never think of questions that they want to answer about the problem. Another extreme is they ask questions they cannot answer. Without question, you can never better understand your problem. Bad questions or no questions means no answers.

Generally, there are three types of quantitative research questions while qualitative is more flexible. If a student does not know this they have no clue how to even begin to explore their problem.

Issues with Measurement

Let’s say a student does know what their questions are, the next mystery for many is measuring the variables if the study is quantitative. This is were applying statistical knowledge rather than simply taking quizzes and test comes to play. The typical student does not understand often how to operationalize their variables and determine what type of variables they will include in their study. If you don’t know how you will measure your variables you cannot answer any questions about your problem.

Lost at the Analysis Stage

The measurement affects the analysis. I cannot tell you how many times a student or even a colleague wanted me to analyze their data without telling me what the research questions were. How can you find answers without questions? The type of measurement affects the potential ways of analyzing data. How you summary categorical data is different from continuous data. Lacking this knowledge leads to inaction.

No Plan for the Write-Up

If a student makes it to this stage, firstly congratulations are in order, however, many students have no idea what to report or how. This is because students lose track of the purpose of their study which was to answer their research questions about the problem. Therefore, in the write-up, you present the answers systematically. First, you answer question 1, then 2, etc.

If necessary you include visuals of the answers. Again Visuals are determined by the type of variable as well as the type of question. A top reason for article rejection is an unclear write-up. Therefore, great care is needed in order for this process to be successful.

Conclusion

Whenever I deal with research students I often walk through these six concepts. Most students never make it past the second or third concept. Perhaps the results will differ for others.

Successful research writing requires the ability to see the big picture and connection the various section of a paper so that the present a cohesive whole. Too many students focus on the little details and forget the purpose of their study. Losing the main idea makes the details worthless.

If I left out any common problems with research please add them in the comments section.

Local Regression in R

Local regression uses something similar to nearest neighbor classification to generate a regression line. In local regression, nearby observations are used to fit the line rather than all observations. It is necessary to indicate the percentage of the observations you want R to use for fitting the local line. The name for this hyperparameter is the span. The higher the span the smoother the line becomes.

Local regression is great one there are only a handful of independent variables in the model. When the total number of variables becomes too numerous the model will struggle. As such, we will only fit a bivariate model. This will allow us to process the model and to visualize it.

In this post, we will use the “Clothing” dataset from the “Ecdat” package and we will examine innovation (inv2) relationship with total sales (tsales). Below is some initial code.

library(Ecdat)
data(Clothing)
str(Clothing)
## 'data.frame':    400 obs. of  13 variables:
##  $ tsales : int  750000 1926395 1250000 694227 750000 400000 1300000 495340 1200000 495340 ...
##  $ sales  : num  4412 4281 4167 2670 15000 ...
##  $ margin : num  41 39 40 40 44 41 39 28 41 37 ...
##  $ nown   : num  1 2 1 1 2 ...
##  $ nfull  : num  1 2 2 1 1.96 ...
##  $ npart  : num  1 3 2.22 1.28 1.28 ...
##  $ naux   : num  1.54 1.54 1.41 1.37 1.37 ...
##  $ hoursw : int  76 192 114 100 104 72 161 80 158 87 ...
##  $ hourspw: num  16.8 22.5 17.2 21.5 15.7 ...
##  $ inv1   : num  17167 17167 292857 22207 22207 ...
##  $ inv2   : num  27177 27177 71571 15000 10000 ...
##  $ ssize  : int  170 450 300 260 50 90 400 100 450 75 ...
##  $ start  : num  41 39 40 40 44 41 39 28 41 37 ...

There is no data preparation in this example. The first thing we will do is fit two different models that have different values for the span hyperparameter. “fit” will have a span of .41 which means it will use 41% of the nearest examples. “fit2” will use .82. Below is the code.

fit<-loess(tsales~inv2,span = .41,data = Clothing)
fit2<-loess(tsales~inv2,span = .82,data = Clothing)

In the code above, we used the “loess” function to fit the model. The “span” argument was set to .41 and .82.

We now need to prepare for the visualization. We begin by using the “range” function to find the distance from the lowest to the highest value. Then use the “seq” function to create a grid. Below is the code.

inv2lims<-range(Clothing$inv2)
inv2.grid<-seq(from=inv2lims[1],to=inv2lims[2])

The information in the code above is for setting our x-axis in the plot. We are now ready to fit our model. We will fit the models and draw each regression line.

plot(Clothing$inv2,Clothing$tsales,xlim=inv2lims)
lines(inv2.grid,predict(fit,data.frame(inv2=inv2.grid)),col='blue',lwd=3)
lines(inv2.grid,predict(fit2,data.frame(inv2=inv2.grid)),col='red',lwd=3)

1

Not much difference in the two models. For our final task, we will predict with our “fit” model using all possible values of “inv2” and also fit the confidence interval lines.

pred<-predict(fit,newdata=inv2.grid,se=T)
plot(Clothing$inv2,Clothing$tsales)
lines(inv2.grid,pred$fit,col='red',lwd=3)
lines(inv2.grid,pred$fit+2*pred$se.fit,lty="dashed",lwd=2,col='blue')
lines(inv2.grid,pred$fit-2*pred$se.fit,lty="dashed",lwd=2,col='blue')

1

Conclusion

Local regression provides another way to model complex non-linear relationships in low dimensions. The example here provides just the basics of how this is done is much more complicated than described here.

Smoothing Splines in R

This post will provide information on smoothing splines. Smoothing splines are used in regression when we want to reduce the residual sum of squares by adding more flexibility to the regression line without allowing too much overfitting.

In order to do this, we must tune the parameter called the smoothing spline. The smoothing spline is essentially a natural cubic spline with a knot at every unique value of x in the model. Having this many knots can lead to severe overfitting. This is corrected for by controlling the degrees of freedom through the parameter called lambda. You can manually set this value or select it through cross-validation.

We will now look at an example of the use of smoothing splines with the “Clothing” dataset from the “Ecdat” package. We want to predict “tsales” based on the use of innovation in the stores. Below is some initial code.

library(Ecdat)
data(Clothing)
str(Clothing)
## 'data.frame':    400 obs. of  13 variables:
##  $ tsales : int  750000 1926395 1250000 694227 750000 400000 1300000 495340 1200000 495340 ...
##  $ sales  : num  4412 4281 4167 2670 15000 ...
##  $ margin : num  41 39 40 40 44 41 39 28 41 37 ...
##  $ nown   : num  1 2 1 1 2 ...
##  $ nfull  : num  1 2 2 1 1.96 ...
##  $ npart  : num  1 3 2.22 1.28 1.28 ...
##  $ naux   : num  1.54 1.54 1.41 1.37 1.37 ...
##  $ hoursw : int  76 192 114 100 104 72 161 80 158 87 ...
##  $ hourspw: num  16.8 22.5 17.2 21.5 15.7 ...
##  $ inv1   : num  17167 17167 292857 22207 22207 ...
##  $ inv2   : num  27177 27177 71571 15000 10000 ...
##  $ ssize  : int  170 450 300 260 50 90 400 100 450 75 ...
##  $ start  : num  41 39 40 40 44 41 39 28 41 37 ...

We are going to create three models. Model one will have 70 degrees of freedom, model two will have 7, and model three will have the number of degrees of freedom are determined through cross-validation. Below is the code.

fit1<-smooth.spline(Clothing$inv2,Clothing$tsales,df=57)
fit2<-smooth.spline(Clothing$inv2,Clothing$tsales,df=7)
fit3<-smooth.spline(Clothing$inv2,Clothing$tsales,cv=T)
## Warning in smooth.spline(Clothing$inv2, Clothing$tsales, cv = T): cross-
## validation with non-unique 'x' values seems doubtful
(data.frame(fit1$df,fit2$df,fit3$df))
##   fit1.df  fit2.df  fit3.df
## 1      57 7.000957 2.791762

In the code above we used the “smooth.spline” function which comes with base r.Notice that we did not use the same coding syntax as the “lm” function calls for. The code above also indicates the degrees of freedom for each model.  You can see that for “fit3” the cross-validation determine that 2.79 was the most appropriate degrees of freedom. In addition, if you type in the following code.

sapply(data.frame(fit1$x,fit2$x,fit3$x),length)
## fit1.x fit2.x fit3.x 
##     73     73     73

You will see that there are only 73 data points in each model. The “Clothing” dataset has 400 examples in it. The reason for this reduction is that the “smooth.spline” function only takes unique values from the original dataset. As such, though there are 400 examples in the dataset only 73 of them are unique.

Next, we plot our data and add regression lines

plot(Clothing$inv2,Clothing$tsales)
lines(fit1,col='red',lwd=3)
lines(fit2,col='green',lwd=3)
lines(fit3,col='blue',lwd=3)
legend('topright',lty=1,col=c('red','green','blue'),c("df = 57",'df=7','df=CV 2.8'))

1.png

You can see that as the degrees of freedom increase so does the flexibility in the line. The advantage of smoothing splines is to have a more flexible way to assess the characteristics of a dataset.

Polynomial Spline Regression in R

Normally, when least squares regression is used, you fit one line to the model. However, sometimes you may want enough flexibility that you fit different lines over different regions of your independent variable. This process of fitting different lines over different regions of X is known as Regression Splines.

How this works is that there are different coefficient values based on the regions of X. As the researcher, you can set the cutoff points for each region. The cutoff point is called a “knot.” The more knots you use the more flexible the model becomes because there are fewer data points with each range allowing for more variability.

We will now go through an example of polynomial regression splines. Remeber that polynomial means that we will have a curved line as we are using higher order polynomials. Our goal will be to predict total sales based on the amount of innovation a store employs. We will use the “Ecdat” package and the “Clothing” dataset. In addition, we will need the “splines” package. The code is as follows.

library(splines);library(Ecdat)
data(Clothing)

We will now fit our model. We must indicate the number and placement of the knots. This is commonly down at the 25th 50th and 75th percentile. Below is the code

fit<-lm(tsales~bs(inv2,knots = c(12000,60000,150000)),data = Clothing)

In the code above we used the traditional “lm” function to set the model. However, we also used the “bs” function which allows us to create our spline regression model. The argument “knots” was set to have three different values. Lastly, the dataset was indicated.

Remember that the default spline model in R is a third-degree polynomial. This is because it is hard for the eye to detect the discontinuity at the knots.

We now need X values that we can use for prediction purposes. In the code below we first find the range of the “inv2” variable. We then create a grid that includes all the possible values of “inv2” in increments of 1. Lastly, we use the “predict” function to develop the prediction model. We set the “se” argument to true as we will need this information. The code is below.

inv2lims<-range(Clothing$inv2)
inv2.grid<-seq(from=inv2lims[1],to=inv2lims[2])
pred<-predict(fit,newdata=list(inv2=inv2.grid),se=T)

We are now ready to plot our model. The code below graphs the model and includes the regression line (red), confidence interval (green), as well as the location of each knot (blue)

plot(Clothing$inv2,Clothing$tsales,main="Regression Spline Plot")
lines(inv2.grid,pred$fit,col='red',lwd=3)
lines(inv2.grid,pred$fit+2*pred$se.fit,lty="dashed",lwd=2,col='green')
lines(inv2.grid,pred$fit-2*pred$se.fit,lty="dashed",lwd=2,col='green')
segments(12000,0,x1=12000,y1=5000000,col='blue' )
segments(60000,0,x1=60000,y1=5000000,col='blue' )
segments(150000,0,x1=150000,y1=5000000,col='blue' )

1.png

When this model was created it was essentially three models connected. Model on goes from the first blue line to the second. Model 2 goes form the second blue line to the third and model three was from the third blue line until the end. This kind of flexibility is valuable in understanding  nonlinear relationship

Logistic Polynomial Regression in R

Polynomial regression is used when you want to develop a regression model that is not linear. It is common to use this method when performing traditional least squares regression. However, it is also possible to use polynomial regression when the dependent variable is categorical. As such, in this post, we will go through an example of logistic polynomial regression.

Specifically, we will use the “Clothing” dataset from the “Ecdat” package. We will divide the “tsales” dependent variable into two categories to run the analysis. Below is the code to get started.

library(Ecdat)
data(Clothing)

There is little preparation for this example. Below is the code for the model

fitglm<-glm(I(tsales>900000)~poly(inv2,4),data=Clothing,family = binomial)

Here is what we did

1. We created an object called “fitglm” to save our results
2. We used the “glm” function to process the model
3. We used the “I” function. This told R to process the information inside the parentheses as is. As such, we did not have to make a new variable in which we split the “tsales” variable. Simply, if sales were greater than 900000 it was code 1 and 0 if less than this amount.
4. Next, we set the information for the independent variable. We used the “poly” function. Inside this function, we placed the “inv2” variable and the highest order polynomial we want to explore.
5. We set the data to “Clothing”
6. Lastly, we set the “family” argument to “binomial” which is needed for logistic regression

Below is the results

summary(fitglm)
## 
## Call:
## glm(formula = I(tsales > 9e+05) ~ poly(inv2, 4), family = binomial, 
##     data = Clothing)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.5025  -0.8778  -0.8458   1.4534   1.5681  
## 
## Coefficients:
##                Estimate Std. Error z value Pr(>|z|)  
## (Intercept)       3.074      2.685   1.145   0.2523  
## poly(inv2, 4)1  641.710    459.327   1.397   0.1624  
## poly(inv2, 4)2  585.975    421.723   1.389   0.1647  
## poly(inv2, 4)3  259.700    178.081   1.458   0.1448  
## poly(inv2, 4)4   73.425     44.206   1.661   0.0967 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 521.57  on 399  degrees of freedom
## Residual deviance: 493.51  on 395  degrees of freedom
## AIC: 503.51
## 
## Number of Fisher Scoring iterations: 13

It appears that only the 4th-degree polynomial is significant and barely at that. We will now find the range of our independent variable “inv2” and make a grid from this information. Doing this will allow us to run our model using the full range of possible values for our independent variable.

inv2lims<-range(Clothing$inv2)
inv2.grid<-seq(from=inv2lims[1],to=inv2lims[2])

The “inv2lims” object has two values. The lowest value in “inv2” and the highest value. These values serve as the highest and lowest values in our “inv2.grid” object. This means that we have values started at 350 and going to 400000 by 1 in a grid to be used as values for “inv2” in our prediction model. Below is our prediction model.

predsglm<-predict(fitglm,newdata=list(inv2=inv2.grid),se=T,type="response")

Next, we need to calculate the probabilities that a given value of “inv2” predicts a store has “tsales” greater than 900000. The equation is as follows.

pfit<-exp(predsglm$fit)/(1+exp(predsglm$fit))

Graphing this leads to interesting insights. Below is the code

plot(pfit)

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You can see the curves in the line from the polynomial expression. As it appears. As inv2 increase the probability increase until the values fall between 125000 and 200000. This is interesting, to say the least.

We now need to plot the actual model. First, we need to calculate the confidence intervals. This is done with the code below.

se.bandsglm.logit<-cbind(predsglm$fit+2*predsglm$se.fit,predsglm$fit-2*predsglm$se.fit)
se.bandsglm<-exp(se.bandsglm.logit)/(1+exp(se.bandsglm.logit))

The ’se.bandsglm” object contains the log odds of each example and the “se.bandsglm” has the probabilities. Now we plot the results

plot(Clothing$inv2,I(Clothing$tsales>900000),xlim=inv2lims,type='n')
points(jitter(Clothing$inv2),I((Clothing$tsales>900000)),cex=2,pch='|',col='darkgrey')
lines(inv2.grid,pfit,lwd=4)
matlines(inv2.grid,se.bandsglm,col="green",lty=6,lwd=6)

1.pngIn the code above we did the following.
1. We plotted our dependent and independent variables. However, we set the argument “type” to n which means nothing. This was done so we can add the information step-by-step.
2. We added the points. This was done using the “points” function. The “jitter” function just helps to spread the information out. The other arguments (cex, pch, col) our for aesthetics and our optional.
3. We add our logistic polynomial line based on our independent variable grid and the “pfit” object which has all of the predicted probabilities.
4. Last, we add the confidence intervals using the “matlines” function. Which includes the grid object as well as the “se.bandsglm” information.

You can see that these results are similar to when we only graphed the “pfit” information. However, we also add the confidence intervals. You can see the same dip around 125000-200000 were there is also a larger confidence interval. if you look at the plot you can see that there are fewer data points in this range which may be what is making the intervals wider.

Conclusion

Logistic polynomial regression allows the regression line to have more curves to it if it is necessary. This is useful for fitting data that is non-linear in nature.

Polynomial Regression in R

Polynomial regression is one of the easiest ways to fit a non-linear line to a data set. This is done through the use of higher order polynomials such as cubic, quadratic, etc to one or more predictor variables in a model.

Generally, polynomial regression is used for one predictor and one outcome variable. When there are several predictor variables it is more common to use generalized additive modeling/ In this post, we will use the “Clothing” dataset from the “Ecdat” package to predict total sales with the use of polynomial regression. Below is some initial code.

library(Ecdat)
data(Clothing)
str(Clothing)
## 'data.frame':    400 obs. of  13 variables:
##  $ tsales : int  750000 1926395 1250000 694227 750000 400000 1300000 495340 1200000 495340 ...
##  $ sales  : num  4412 4281 4167 2670 15000 ...
##  $ margin : num  41 39 40 40 44 41 39 28 41 37 ...
##  $ nown   : num  1 2 1 1 2 ...
##  $ nfull  : num  1 2 2 1 1.96 ...
##  $ npart  : num  1 3 2.22 1.28 1.28 ...
##  $ naux   : num  1.54 1.54 1.41 1.37 1.37 ...
##  $ hoursw : int  76 192 114 100 104 72 161 80 158 87 ...
##  $ hourspw: num  16.8 22.5 17.2 21.5 15.7 ...
##  $ inv1   : num  17167 17167 292857 22207 22207 ...
##  $ inv2   : num  27177 27177 71571 15000 10000 ...
##  $ ssize  : int  170 450 300 260 50 90 400 100 450 75 ...
##  $ start  : num  41 39 40 40 44 41 39 28 41 37 ...

We are going to use the “inv2” variable as our predictor. This variable measures the investment in automation by a particular store. We will now run our polynomial regression model.

fit<-lm(tsales~poly(inv2,5),data = Clothing)
summary(fit)
## 
## Call:
## lm(formula = tsales ~ poly(inv2, 5), data = Clothing)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -946668 -336447  -96763  184927 3599267 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      833584      28489  29.259  < 2e-16 ***
## poly(inv2, 5)1  2391309     569789   4.197 3.35e-05 ***
## poly(inv2, 5)2  -665063     569789  -1.167   0.2438    
## poly(inv2, 5)3    49793     569789   0.087   0.9304    
## poly(inv2, 5)4  1279190     569789   2.245   0.0253 *  
## poly(inv2, 5)5  -341189     569789  -0.599   0.5497    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 569800 on 394 degrees of freedom
## Multiple R-squared:  0.05828,    Adjusted R-squared:  0.04633 
## F-statistic: 4.876 on 5 and 394 DF,  p-value: 0.0002428

The code above should be mostly familiar. We use the “lm” function as normal for regression. However, we then used the “poly” function on the “inv2” variable. What this does is runs our model 5 times (5 is the number next to “inv2”). Each time a different polynomial is used from 1 (no polynomial) to 5 (5th order polynomial). The results indicate that the 4th-degree polynomial is significant.

We now will prepare a visual of the results but first, there are several things we need to prepare. First, we want to find what the range of our predictor variable “inv2” is and we will save this information in a grade. The code is below.

inv2lims<-range(Clothing$inv2)

Second, we need to create a grid that has all the possible values of “inv2” from the lowest to the highest broken up in intervals of one. Below is the code.

inv2.grid<-seq(from=inv2lims[1],to=inv2lims[2])

We now have a dataset with almost 400000 data points in the “inv2.grid” object through this approach. We will now use these values to predict “tsales.” We also want the standard errors so we se “se” to TRUE

preds<-predict(fit,newdata=list(inv2=inv2.grid),se=TRUE)

We now need to find the confidence interval for our regression line. This is done by making a dataframe that takes the predicted fit adds or subtracts 2 and multiples this number by the standard error as shown below.

se.bands<-cbind(preds$fit+2*preds$se.fit,preds$fit-2*preds$se.fit)

With these steps completed, we are ready to create our civilization.

To make our visual, we use the “plot” function on the predictor and outcome. Doing this gives us a plot without a regression line. We then use the “lines” function to add the polynomial regression line, however, this line is based on the “inv2.grid” object (40,000 observations) and our predictions. Lastly, we use the “matlines” function to add the confidence intervals we found and stored in the “se.bands” object.

plot(Clothing$inv2,Clothing$tsales)
lines(inv2.grid,preds$fit,lwd=4,col='blue')
matlines(inv2.grid,se.bands,lwd = 4,col = "yellow",lty=4)

1.png

Conclusion

You can clearly see the curvature of the line. Which helped to improve model fit. Now any of you can tell that we are fitting this line to mostly outliers. This is one reason we the standard error gets wider and wider it is because there are fewer and fewer observations on which to base it. However, for demonstration purposes, this is a clear example of the power of polynomial regression.

Partial Least Squares Regression in R

Partial least squares regression is a form of regression that involves the development of components of the original variables in a supervised way. What this means is that the dependent variable is used to help create the new components form the original variables. This means that when pls is used the linear combination of the new features helps to explain both the independent and dependent variables in the model.

In this post, we will use predict “income” in the “Mroz” dataset using pls. Below is some initial code.

library(pls);library(Ecdat)
data("Mroz")
str(Mroz)
## 'data.frame':    753 obs. of  18 variables:
##  $ work      : Factor w/ 2 levels "yes","no": 2 2 2 2 2 2 2 2 2 2 ...
##  $ hoursw    : int  1610 1656 1980 456 1568 2032 1440 1020 1458 1600 ...
##  $ child6    : int  1 0 1 0 1 0 0 0 0 0 ...
##  $ child618  : int  0 2 3 3 2 0 2 0 2 2 ...
##  $ agew      : int  32 30 35 34 31 54 37 54 48 39 ...
##  $ educw     : int  12 12 12 12 14 12 16 12 12 12 ...
##  $ hearnw    : num  3.35 1.39 4.55 1.1 4.59 ...
##  $ wagew     : num  2.65 2.65 4.04 3.25 3.6 4.7 5.95 9.98 0 4.15 ...
##  $ hoursh    : int  2708 2310 3072 1920 2000 1040 2670 4120 1995 2100 ...
##  $ ageh      : int  34 30 40 53 32 57 37 53 52 43 ...
##  $ educh     : int  12 9 12 10 12 11 12 8 4 12 ...
##  $ wageh     : num  4.03 8.44 3.58 3.54 10 ...
##  $ income    : int  16310 21800 21040 7300 27300 19495 21152 18900 20405 20425 ...
##  $ educwm    : int  12 7 12 7 12 14 14 3 7 7 ...
##  $ educwf    : int  7 7 7 7 14 7 7 3 7 7 ...
##  $ unemprate : num  5 11 5 5 9.5 7.5 5 5 3 5 ...
##  $ city      : Factor w/ 2 levels "no","yes": 1 2 1 1 2 2 1 1 1 1 ...
##  $ experience: int  14 5 15 6 7 33 11 35 24 21 ...

First, we must prepare our data by dividing it into a training and test set. We will do this by doing a 50/50 split of the data.

set.seed(777)
train<-sample(c(T,F),nrow(Mroz),rep=T) #50/50 train/test split
test<-(!train)

In the code above we set the “set.seed function in order to assure reduplication. Then we created the “train” object and used the “sample” function to make a vector with ‘T’ and ‘F’ based on the number of rows in “Mroz”. Lastly, we created the “test”” object base don everything that is not in the “train” object as that is what the exclamation point is for.

Now we create our model using the “plsr” function from the “pls” package and we will examine the results using the “summary” function. We will also scale the data since this the scale affects the development of the components and use cross-validation. Below is the code.

set.seed(777)
pls.fit<-plsr(income~.,data=Mroz,subset=train,scale=T,validation="CV")
summary(pls.fit)
## Data:    X dimension: 392 17 
##  Y dimension: 392 1
## Fit method: kernelpls
## Number of components considered: 17
## 
## VALIDATION: RMSEP
## Cross-validated using 10 random segments.
##        (Intercept)  1 comps  2 comps  3 comps  4 comps  5 comps  6 comps
## CV           11218     8121     6701     6127     5952     5886     5857
## adjCV        11218     8114     6683     6108     5941     5872     5842
##        7 comps  8 comps  9 comps  10 comps  11 comps  12 comps  13 comps
## CV        5853     5849     5854      5853      5853      5852      5852
## adjCV     5837     5833     5837      5836      5836      5835      5835
##        14 comps  15 comps  16 comps  17 comps
## CV         5852      5852      5852      5852
## adjCV      5835      5835      5835      5835
## 
## TRAINING: % variance explained
##         1 comps  2 comps  3 comps  4 comps  5 comps  6 comps  7 comps
## X         17.04    26.64    37.18    49.16    59.63    64.63    69.13
## income    49.26    66.63    72.75    74.16    74.87    75.25    75.44
##         8 comps  9 comps  10 comps  11 comps  12 comps  13 comps  14 comps
## X         72.82    76.06     78.59     81.79     85.52     89.55     92.14
## income    75.49    75.51     75.51     75.52     75.52     75.52     75.52
##         15 comps  16 comps  17 comps
## X          94.88     97.62    100.00
## income     75.52     75.52     75.52

The printout includes the root mean squared error for each of the components in the VALIDATION section as well as the variance explained in the TRAINING section. There are 17 components because there are 17 independent variables. You can see that after component 3 or 4 there is little improvement in the variance explained in the dependent variable. Below is the code for the plot of these results. It requires the use of the “validationplot” function with the “val.type” argument set to “MSEP” Below is the code

validationplot(pls.fit,val.type = "MSEP")

1.png

We will do the predictions with our model. We use the “predict” function, use our “Mroz” dataset but only those index in the “test” vector and set the components to three based on our previous plot. Below is the code.

set.seed(777)
pls.pred<-predict(pls.fit,Mroz[test,],ncomp=3)

After this, we will calculate the mean squared error. This is done by subtracting the results of our predicted model from the dependent variable of the test set. We then square this information and calculate the mean. Below is the code

mean((pls.pred-Mroz$income[test])^2)
## [1] 63386682

As you know, this information is only useful when compared to something else. Therefore, we will run the data with a tradition least squares regression model and compare the results.

set.seed(777)
lm.fit<-lm(income~.,data=Mroz,subset=train)
lm.pred<-predict(lm.fit,Mroz[test,])
mean((lm.pred-Mroz$income[test])^2)
## [1] 59432814

The least squares model is slightly better then our partial least squares model but if we look at the model we see several variables that are not significant. We will remove these see what the results are

summary(lm.fit)
## 
## Call:
## lm(formula = income ~ ., data = Mroz, subset = train)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -20131  -2923  -1065   1670  36246 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -1.946e+04  3.224e+03  -6.036 3.81e-09 ***
## workno      -4.823e+03  1.037e+03  -4.651 4.59e-06 ***
## hoursw       4.255e+00  5.517e-01   7.712 1.14e-13 ***
## child6      -6.313e+02  6.694e+02  -0.943 0.346258    
## child618     4.847e+02  2.362e+02   2.052 0.040841 *  
## agew         2.782e+02  8.124e+01   3.424 0.000686 ***
## educw        1.268e+02  1.889e+02   0.671 0.502513    
## hearnw       6.401e+02  1.420e+02   4.507 8.79e-06 ***
## wagew        1.945e+02  1.818e+02   1.070 0.285187    
## hoursh       6.030e+00  5.342e-01  11.288  < 2e-16 ***
## ageh        -9.433e+01  7.720e+01  -1.222 0.222488    
## educh        1.784e+02  1.369e+02   1.303 0.193437    
## wageh        2.202e+03  8.714e+01  25.264  < 2e-16 ***
## educwm      -4.394e+01  1.128e+02  -0.390 0.697024    
## educwf       1.392e+02  1.053e+02   1.322 0.186873    
## unemprate   -1.657e+02  9.780e+01  -1.694 0.091055 .  
## cityyes     -3.475e+02  6.686e+02  -0.520 0.603496    
## experience  -1.229e+02  4.490e+01  -2.737 0.006488 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5668 on 374 degrees of freedom
## Multiple R-squared:  0.7552, Adjusted R-squared:  0.744 
## F-statistic: 67.85 on 17 and 374 DF,  p-value: < 2.2e-16
set.seed(777)
lm.fit<-lm(income~work+hoursw+child618+agew+hearnw+hoursh+wageh+experience,data=Mroz,subset=train)
lm.pred<-predict(lm.fit,Mroz[test,])
mean((lm.pred-Mroz$income[test])^2)
## [1] 57839715

As you can see the error decreased even more which indicates that the least squares regression model is superior to the partial least squares model. In addition, the partial least squares model is much more difficult to explain because of the use of components. As such, the least squares model is the favored one.

Principal Component Regression in R

This post will explain and provide an example of principal component regression (PCR). Principal component regression involves having the model construct components from the independent variables that are a linear combination of the independent variables. This is similar to principal component analysis but the components are designed in a way to best explain the dependent variable. Doing this often allows you to use fewer variables in your model and usually improves the fit of your model as well.

Since PCR is based on principal component analysis it is an unsupervised method, which means the dependent variable has no influence on the development of the components. As such, there are times when the components that are developed may not be beneficial for explaining the dependent variable.

Our example will use the “Mroz” dataset from the “Ecdat” package. Our goal will be to predict “income” based on the variables in the dataset. Below is the initial code

library(pls);library(Ecdat)
data(Mroz)
str(Mroz)
## 'data.frame':    753 obs. of  18 variables:
##  $ work      : Factor w/ 2 levels "yes","no": 2 2 2 2 2 2 2 2 2 2 ...
##  $ hoursw    : int  1610 1656 1980 456 1568 2032 1440 1020 1458 1600 ...
##  $ child6    : int  1 0 1 0 1 0 0 0 0 0 ...
##  $ child618  : int  0 2 3 3 2 0 2 0 2 2 ...
##  $ agew      : int  32 30 35 34 31 54 37 54 48 39 ...
##  $ educw     : int  12 12 12 12 14 12 16 12 12 12 ...
##  $ hearnw    : num  3.35 1.39 4.55 1.1 4.59 ...
##  $ wagew     : num  2.65 2.65 4.04 3.25 3.6 4.7 5.95 9.98 0 4.15 ...
##  $ hoursh    : int  2708 2310 3072 1920 2000 1040 2670 4120 1995 2100 ...
##  $ ageh      : int  34 30 40 53 32 57 37 53 52 43 ...
##  $ educh     : int  12 9 12 10 12 11 12 8 4 12 ...
##  $ wageh     : num  4.03 8.44 3.58 3.54 10 ...
##  $ income    : int  16310 21800 21040 7300 27300 19495 21152 18900 20405 20425 ...
##  $ educwm    : int  12 7 12 7 12 14 14 3 7 7 ...
##  $ educwf    : int  7 7 7 7 14 7 7 3 7 7 ...
##  $ unemprate : num  5 11 5 5 9.5 7.5 5 5 3 5 ...
##  $ city      : Factor w/ 2 levels "no","yes": 1 2 1 1 2 2 1 1 1 1 ...
##  $ experience: int  14 5 15 6 7 33 11 35 24 21 ...

Our first step is to divide our dataset into a train and test set. We will do a simple 50/50 split for this demonstration.

train<-sample(c(T,F),nrow(Mroz),rep=T) #50/50 train/test split
test<-(!train)

In the code above we use the “sample” function to create a “train” index based on the number of rows in the “Mroz” dataset. Basically, R is making a vector that randomly assigns different rows in the “Mroz” dataset to be marked as True or False. Next, we use the “train” vector and we assign everything or every number that is not in the “train” vector to the test vector by using the exclamation mark.

We are now ready to develop our model. Below is the code

set.seed(777)
pcr.fit<-pcr(income~.,data=Mroz,subset=train,scale=T,validation="CV")

To make our model we use the “pcr” function from the “pls” package. The “subset” argument tells r to use the “train” vector to select examples from the “Mroz” dataset. The “scale” argument makes sure everything is measured the same way. This is important when using a component analysis tool as variables with different scale have a different influence on the components. Lastly, the “validation” argument enables cross-validation. This will help us to determine the number of components to use for prediction. Below is the results of the model using the “summary” function.

summary(pcr.fit)
## Data:    X dimension: 381 17 
##  Y dimension: 381 1
## Fit method: svdpc
## Number of components considered: 17
## 
## VALIDATION: RMSEP
## Cross-validated using 10 random segments.
##        (Intercept)  1 comps  2 comps  3 comps  4 comps  5 comps  6 comps
## CV           12102    11533    11017     9863     9884     9524     9563
## adjCV        12102    11534    11011     9855     9878     9502     9596
##        7 comps  8 comps  9 comps  10 comps  11 comps  12 comps  13 comps
## CV        9149     9133     8811      8527      7265      7234      7120
## adjCV     9126     9123     8798      8877      7199      7172      7100
##        14 comps  15 comps  16 comps  17 comps
## CV         7118      7141      6972      6992
## adjCV      7100      7123      6951      6969
## 
## TRAINING: % variance explained
##         1 comps  2 comps  3 comps  4 comps  5 comps  6 comps  7 comps
## X        21.359    38.71    51.99    59.67    65.66    71.20    76.28
## income    9.927    19.50    35.41    35.63    41.28    41.28    46.75
##         8 comps  9 comps  10 comps  11 comps  12 comps  13 comps  14 comps
## X         80.70    84.39     87.32     90.15     92.65     95.02     96.95
## income    47.08    50.98     51.73     68.17     68.29     68.31     68.34
##         15 comps  16 comps  17 comps
## X          98.47     99.38    100.00
## income     68.48     70.29     70.39

There is a lot of information here.The VALIDATION: RMSEP section gives you the root mean squared error of the model broken down by component. The TRAINING section is similar the printout of any PCA but it shows the amount of cumulative variance of the components, as well as the variance, explained for the dependent variable “income.” In this model, we are able to explain up to 70% of the variance if we use all 17 components.

We can graph the MSE using the “validationplot” function with the argument “val.type” set to “MSEP”. The code is below.

validationplot(pcr.fit,val.type = "MSEP")

1

How many components to pick is subjective, however, there is almost no improvement beyond 13 so we will use 13 components in our prediction model and we will calculate the means squared error.

set.seed(777)
pcr.pred<-predict(pcr.fit,Mroz[test,],ncomp=13)
mean((pcr.pred-Mroz$income[test])^2)
## [1] 48958982

MSE is what you would use to compare this model to other models that you developed. Below is the performance of a least squares regression model

set.seed(777)
lm.fit<-lm(income~.,data=Mroz,subset=train)
lm.pred<-predict(lm.fit,Mroz[test,])
mean((lm.pred-Mroz$income[test])^2)
## [1] 47794472

If you compare the MSE the least squares model performs slightly better than the PCR one. However, there are a lot of non-significant features in the model as shown below.

summary(lm.fit)
## 
## Call:
## lm(formula = income ~ ., data = Mroz, subset = train)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -27646  -3337  -1387   1860  48371 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2.215e+04  3.987e+03  -5.556 5.35e-08 ***
## workno      -3.828e+03  1.316e+03  -2.909  0.00385 ** 
## hoursw       3.955e+00  7.085e-01   5.582 4.65e-08 ***
## child6       5.370e+02  8.241e+02   0.652  0.51512    
## child618     4.250e+02  2.850e+02   1.491  0.13673    
## agew         1.962e+02  9.849e+01   1.992  0.04709 *  
## educw        1.097e+02  2.276e+02   0.482  0.63013    
## hearnw       9.835e+02  2.303e+02   4.270 2.50e-05 ***
## wagew        2.292e+02  2.423e+02   0.946  0.34484    
## hoursh       6.386e+00  6.144e-01  10.394  < 2e-16 ***
## ageh        -1.284e+01  9.762e+01  -0.132  0.89542    
## educh        1.460e+02  1.592e+02   0.917  0.35982    
## wageh        2.083e+03  9.930e+01  20.978  < 2e-16 ***
## educwm       1.354e+02  1.335e+02   1.014  0.31115    
## educwf       1.653e+02  1.257e+02   1.315  0.18920    
## unemprate   -1.213e+02  1.148e+02  -1.057  0.29140    
## cityyes     -2.064e+02  7.905e+02  -0.261  0.79421    
## experience  -1.165e+02  5.393e+01  -2.159  0.03147 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6729 on 363 degrees of freedom
## Multiple R-squared:  0.7039, Adjusted R-squared:   0.69 
## F-statistic: 50.76 on 17 and 363 DF,  p-value: < 2.2e-16

Removing these and the MSE is almost the same for the PCR and least square models

set.seed(777)
lm.fit2<-lm(income~work+hoursw+hearnw+hoursh+wageh,data=Mroz,subset=train)
lm.pred2<-predict(lm.fit2,Mroz[test,])
mean((lm.pred2-Mroz$income[test])^2)
## [1] 47968996

Conclusion

Since the least squares model is simpler it is probably the superior model. PCR is strongest when there are a lot of variables involve and if there are issues with multicollinearity.

Example of Best Subset Regression in R

This post will provide an example of best subset regression. This is a topic that has been covered before in this blog. However, in the current post, we will approach this using a slightly different coding and a different dataset. We will be using the “HI” dataset from the “Ecdat” package. Our goal will be to predict the number of hours a women works based on the other variables in the dataset. Below is some initial code.

library(leaps);library(Ecdat)
data(HI)
str(HI)
## 'data.frame':    22272 obs. of  13 variables:
##  $ whrswk    : int  0 50 40 40 0 40 40 25 45 30 ...
##  $ hhi       : Factor w/ 2 levels "no","yes": 1 1 2 1 2 2 2 1 1 1 ...
##  $ whi       : Factor w/ 2 levels "no","yes": 1 2 1 2 1 2 1 1 2 1 ...
##  $ hhi2      : Factor w/ 2 levels "no","yes": 1 1 2 2 2 2 2 1 1 2 ...
##  $ education : Ord.factor w/ 6 levels "<9years"<"9-11years"<..: 4 4 3 4 2 3 5 3 5 4 ...
##  $ race      : Factor w/ 3 levels "white","black",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ hispanic  : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
##  $ experience: num  13 24 43 17 44.5 32 14 1 4 7 ...
##  $ kidslt6   : int  2 0 0 0 0 0 0 1 0 1 ...
##  $ kids618   : int  1 1 0 1 0 0 0 0 0 0 ...
##  $ husby     : num  12 1.2 31.3 9 0 ...
##  $ region    : Factor w/ 4 levels "other","northcentral",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ wght      : int  214986 210119 219955 210317 219955 208148 213615 181960 214874 214874 ...

To develop a model we use the “regsubset” function from the “leap” package. Most of the coding is the same as linear regression. The only difference is the “nvmax” argument which is set to 13. The default setting for “nvmax” is 8. This is good if you only have 8 variables. However, the results from the “str” function indicate that we have 13 functions. Therefore, we need to set the “nvmax” argument to 13 instead of the default value of 8 in order to be sure to include all variables. Below is the code

regfit.full<-regsubsets(whrswk~.,HI, nvmax = 13)

We can look at the results with the “summary” function. For space reasons, the code is shown but the results will not be shown here.

summary(regfit.full)

If you run the code above in your computer you will 13 columns that are named after the variables created. A star in a column means that that variable is included in the model. To the left is the numbers 1-13 which. One means one variable in the model two means two variables in the model etc.

Our next step is to determine which of these models is the best. First, we need to decide what our criteria for inclusion will be. Below is a list of available fit indices.

names(summary(regfit.full))
## [1] "which"  "rsq"    "rss"    "adjr2"  "cp"     "bic"    "outmat" "obj"

For our purposes, we will use “rsq” (r-square) and “bic” “Bayesian Information Criteria.” In the code below we are going to save the values for these two fit indices in their own objects.

rsq<-summary(regfit.full)$rsq
bic<-summary(regfit.full)$bic

Now let’s plot them

plot(rsq,type='l',main="R-Square",xlab="Number of Variables")

1

plot(bic,type='l',main="BIC",xlab="Number of Variables")

1.png

You can see that for r-square the values increase and for BIC the values decrease. We will now make both of these plots again but we will have r tell the optimal number of variables when considering each model index. For we use the “which” function to determine the max r-square and the minimum BIC

which.max(rsq)
## [1] 13
which.min(bic)
## [1] 12

The model with the best r-square is the one with 13 variables. This makes sense as r-square always improves as you add variables. Since this is a demonstration we will not correct for this. For BIC the lowest values was for 12 variables. We will now plot this information and highlight the best model in the plot using the “points” function, which allows you to emphasis one point in a graph

plot(rsq,type='l',main="R-Square with Best Model Highlighted",xlab="Number of Variables")
points(13,(rsq[13]),col="blue",cex=7,pch=20)

1.png

plot(bic,type='l',main="BIC with Best Model Highlighted",xlab="Number of Variables")
points(12,(bic[12]),col="blue",cex=7,pch=20)

1.png

Since BIC calls for only 12 variables it is simpler than the r-square recommendation of 13. Therefore, we will fit our final model using the BIC recommendation of 12. Below is the code.

coef(regfit.full,12)
##        (Intercept)             hhiyes             whiyes 
##        30.31321796         1.16940604        18.25380263 
##        education.L        education^4        education^5 
##         6.63847641         1.54324869        -0.77783663 
##          raceblack        hispanicyes         experience 
##         3.06580207        -1.33731802        -0.41883100 
##            kidslt6            kids618              husby 
##        -6.02251640        -0.82955827        -0.02129349 
## regionnorthcentral 
##         0.94042820

So here is our final model. This is what we would use for our test set.

Conclusion

Best subset regression provides the researcher with insights into every possible model as well as clues as to which model is at least statistically superior. This knowledge can be used for developing models for data science applications.

High Dimensionality Regression

There are times when least squares regression is not able to provide accurate predictions or explanation in an object. One example in which least scares regression struggles with a small sample size. By small, we mean when the total number of variables is greater than the sample size. Another term for this is high dimensions which means more variables than examples in the dataset

This post will explain the consequences of what happens when high dimensions is a problem and also how to address the problem.

Inaccurate measurements

One problem with high dimensions in regression is that the results for the various metrics are overfitted to the data. Below is an example using the “attitude” dataset. There are 2 variables and 3 examples for developing a model. This is not strictly high dimensions but it is an example of a small sample size.

data("attitude")
reg1 <- lm(complaints[1:3]~rating[1:3],data=attitude[1:3]) 
summary(reg1)
## 
## Call:
## lm(formula = complaints[1:3] ~ rating[1:3], data = attitude[1:3])
## 
## Residuals:
##       1       2       3 
##  0.1026 -0.3590  0.2564 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 21.95513    1.33598   16.43   0.0387 *
## rating[1:3]  0.67308    0.02221   30.31   0.0210 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4529 on 1 degrees of freedom
## Multiple R-squared:  0.9989, Adjusted R-squared:  0.9978 
## F-statistic: 918.7 on 1 and 1 DF,  p-value: 0.021

With only 3 data points the fit is perfect. You can also examine the mean squared error of the model. Below is a function for this followed by the results

mse <- function(sm){ 
        mean(sm$residuals^2)}
mse(reg1)
## [1] 0.06837607

Almost no error. Lastly, let’s look at a visual of the model

with(attitude[1:3],plot(complaints[1:3]~ rating[1:3]))
title(main = "Sample Size 3")
abline(lm(complaints[1:3]~rating[1:3],data = attitude))

1.png

You can see that the regression line goes almost perfectly through each data point. If we tried to use this model on the test set in a real data science problem there would be a huge amount of bias. Now we will rerun the analysis this time with the full sample.

reg2<- lm(complaints~rating,data=attitude) 
summary(reg2)
## 
## Call:
## lm(formula = complaints ~ rating, data = attitude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13.3880  -6.4553  -0.2997   6.1462  13.3603 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   8.2445     7.6706   1.075    0.292    
## rating        0.9029     0.1167   7.737 1.99e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.65 on 28 degrees of freedom
## Multiple R-squared:  0.6813, Adjusted R-squared:  0.6699 
## F-statistic: 59.86 on 1 and 28 DF,  p-value: 1.988e-08

You can clearly see a huge reduction in the r-square from .99 to .68. Next, is the mean-square error

mse(reg2)
## [1] 54.61425

The error has increased a great deal. Lastly, we fit the regression line

with(attitude,plot(complaints~ rating))
title(main = "Full Sample Size")
abline(lm(complaints~rating,data = attitude))

1.png

Naturally, the second model is more likely to perform better with a test set. The problem is that least squares regression is too flexible when the number of features is greater than or equal to the number of examples in a dataset.

What to Do?

If least squares regression must be used. One solution to overcoming high dimensionality is to use some form of regularization regression such as ridge, lasso, or elastic net. Any of these regularization approaches will help to reduce the number of variables or dimensions in the final model through the use of shrinkage.

However, keep in mind that no matter what you do as the number of dimensions increases so does the r-square even if the variable is useless. This is known as the curse of dimensionality. Again, regularization can help with this.

Remember that with a large number of dimensions there are normally several equally acceptable models. To determine which is most useful depends on understanding the problem and context of the study.

Conclusion

With the ability to collect huge amounts of data has led to the growing problem of high dimensionality. One there are more features than examples it can lead to statistical errors. However, regularization is one tool for dealing with this problem.

Regression with Shrinkage Methods

One problem with least squares regression is determining what variables to keep in a model. One solution to this problem is the use of shrinkage methods. Shrinkage regression involves constraining or regularizing the coefficient estimates towards zero. The benefit of this is that it is an efficient way to either remove variables from a model or significantly reduce the influence of less important variables.

In this post, we will look at two common forms of regularization and these are.

Ridge

Ridge regression includes a tuning parameter called lambda that can be used to reduce to weak coefficients almost to zero. This shrinkage penalty helps with the bias-variance trade-off. Lambda can be set to any value from 0 to infinity. A lambda set to 0 is the same as least square regression while a lambda set to infinity will produce a null model. The technical term for lambda when ridge is used is the “l2 norm”

Finding the right value of lambda is the primary goal when using this algorithm,. Finding it involves running models with several values of lambda and seeing which returns the best results on predetermined metrics.

The primary problem with ridge regression is that it does not actually remove any variables from the model. As such, the prediction might be excellent but explanatory power is not improve if there are a large number of variables.

Lasso

Lasso regression has the same characteristics as Ridge with one exception. The one exception is the Lasso algorithm can actually remove variables by setting them to zero. This means that lasso regression models are usually superior in terms of the ability to interpret and explain them. The technical term for lambda when lasso is used is the “l1 norm.”

It is not clear when lasso or ridge is superior. Normally, if the goal is explanatory lasso is often stronger. However, if the goal is prediction, ridge may be an improvement but not always.

Conclusion

Shrinkage methods are not limited to regression. Many other forms of analysis can employ shrinkage such as artificial neural networks. Most machine learning models can accommodate shrinkage.

Generally, ridge and lasso regression is employed when you have a huge number of predictors as well as a larger dataset. The primary goal is the simplification of an overly complex model. Therefore, the shrinkage methods mentioned here are additional ways to use statistical models in regression.

Subset Selection Regression

There are many different ways in which the variables of a regression model can be selected. In this post, we look at several common ways in which to select variables or features for a regression model. In particular, we look at the following.

  • Best subset regression
  • Stepwise selection

Best Subset Regression

Best subset regression fits a regression model for every possible combination of variables. The “best” model can be selected based on such criteria as the adjusted r-square, BIC (Bayesian Information Criteria), etc.

The primary drawback to best subset regression is that it becomes impossible to compute the results when you have a large number of variables. Generally, when the number of variables exceeds 40 best subset regression becomes too difficult to calculate.

Stepwise Selection

Stepwise selection involves adding or taking away one variable at a time from a regression model. There are two forms of stepwise selection and they are forward and backward selection.

In forward selection, the computer starts with a null model ( a model that calculates the mean) and adds one variable at a time to the model. The variable chosen is the one the provides the best improvement to the model fit. This process reduces greatly the number of models that need to be fitted in comparison to best subset regression.

Backward selection starts the full model and removes one variable at a time based on which variable improves the model fit the most. The main problem with either forward or backward selection is that the best model may not always be selected in this process. In addition, backward selection must have a sample size that is larger than the number of variables.

Deciding Which to Choose

Best subset regression is perhaps most appropriate when you have a small number of variables to develop a model with, such as less than 40. When the number of variables grows forward or backward selection are appropriate. If the sample size is small forward selection may be a better choice. However, if the sample size is large as in the number of examples is greater than the number of variables it is now possible to use backward selection.

Conclusion

The examples here are some of the most basic ways to develop a regression model. However, these are not the only ways in which this can be done. What these examples provide is an introduction to regression model development. In addition, these models provide some sort of criteria for the addition or removal of a variable based on statistics rather than intuition.

Additive Assumption and Multiple Regression

In regression, one of the assumptions is the additive assumption. This assumption states that the influence of a predictor variable on the dependent variable is independent of any other influence. However, in practice, it is common that this assumption does not hold.

In this post, we will look at how to address violations of the additive assumption through the use of interactions in a regression model.

An interaction effect is when you have two predictor variables whose effect on the dependent variable is not the same. As such, their effect must be considered simultaneously rather than separately. This is done through the use of an interaction term. An interaction term is the product of the two predictor variables.

Let’s begin by making a regular regression model with an interaction. To do this we will use the “Carseats” data from the “ISLR” package to predict “Sales”. Below is the code.

library(ISLR);library(ggplot2)
data(Carseats)
saleslm<-lm(Sales~.,Carseats)
summary(saleslm)
## 
## Call:
## lm(formula = Sales ~ ., data = Carseats)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.8692 -0.6908  0.0211  0.6636  3.4115 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      5.6606231  0.6034487   9.380  < 2e-16 ***
## CompPrice        0.0928153  0.0041477  22.378  < 2e-16 ***
## Income           0.0158028  0.0018451   8.565 2.58e-16 ***
## Advertising      0.1230951  0.0111237  11.066  < 2e-16 ***
## Population       0.0002079  0.0003705   0.561    0.575    
## Price           -0.0953579  0.0026711 -35.700  < 2e-16 ***
## ShelveLocGood    4.8501827  0.1531100  31.678  < 2e-16 ***
## ShelveLocMedium  1.9567148  0.1261056  15.516  < 2e-16 ***
## Age             -0.0460452  0.0031817 -14.472  < 2e-16 ***
## Education       -0.0211018  0.0197205  -1.070    0.285    
## UrbanYes         0.1228864  0.1129761   1.088    0.277    
## USYes           -0.1840928  0.1498423  -1.229    0.220    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.019 on 388 degrees of freedom
## Multiple R-squared:  0.8734, Adjusted R-squared:  0.8698 
## F-statistic: 243.4 on 11 and 388 DF,  p-value: < 2.2e-16

The results are rather excellent for the social sciences. The model explains 87.3% of the variance in “Sales”. The current results that we have are known as main effects. These are effects that directly influence the dependent variable. Most regression models only include main effects.

We will now examine an interaction effect between two continuous variables. Let’s see if there is an interaction between “Population” and “Income”.

saleslm1<-lm(Sales~CompPrice+Income+Advertising+Population+Price+Age+Education+US+
                     Urban+ShelveLoc+Population*Income, Carseats)
summary(saleslm1)
## 
## Call:
## lm(formula = Sales ~ CompPrice + Income + Advertising + Population + 
##     Price + Age + Education + US + Urban + ShelveLoc + Population * 
##     Income, data = Carseats)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.8699 -0.7624  0.0139  0.6763  3.4344 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        6.195e+00  6.436e-01   9.625   <2e-16 ***
## CompPrice          9.262e-02  4.126e-03  22.449   <2e-16 ***
## Income             7.973e-03  3.869e-03   2.061   0.0400 *  
## Advertising        1.237e-01  1.107e-02  11.181   <2e-16 ***
## Population        -1.811e-03  9.524e-04  -1.901   0.0580 .  
## Price             -9.511e-02  2.659e-03 -35.773   <2e-16 ***
## Age               -4.566e-02  3.169e-03 -14.409   <2e-16 ***
## Education         -2.157e-02  1.961e-02  -1.100   0.2722    
## USYes             -2.160e-01  1.497e-01  -1.443   0.1498    
## UrbanYes           1.330e-01  1.124e-01   1.183   0.2375    
## ShelveLocGood      4.859e+00  1.523e-01  31.901   <2e-16 ***
## ShelveLocMedium    1.964e+00  1.255e-01  15.654   <2e-16 ***
## Income:Population  2.879e-05  1.253e-05   2.298   0.0221 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.013 on 387 degrees of freedom
## Multiple R-squared:  0.8751, Adjusted R-squared:  0.8712 
## F-statistic:   226 on 12 and 387 DF,  p-value: < 2.2e-16

The new contribution is at the bottom of the coefficient table and is the “Income:Population” coefficient. What this means is “the increase of Sales given a one unit increase in Income and Population simultaneously” In other words the “Income:Population” coefficient looks at their combined simultaneous effect on Sales rather than just their independent effect on Sales.

This makes practical sense as well. The larger the population the more available income and vice versa. However, for our current model, the improvement in the r-squared is relatively small. The actual effect is a small increase in sales. Below is a graph of income and population by sales. Notice how the lines cross. This is a visual of what an interaction looks like. The lines are not parallel by any means.

ggplot(data=Carseats, aes(x=Income, y=Sales, group=1)) +geom_smooth(method=lm,se=F)+ 
        geom_smooth(aes(Population,Sales), method=lm, se=F,color="black")+xlab("Income and Population")+labs(
                title="Income in Blue Population in Black")

We will now repeat this process but this time using a categorical variable and a continuous variable. We will look at the interaction between “US” location (categorical) and “Advertising” (continuous).

saleslm2<-lm(Sales~CompPrice+Income+Advertising+Population+Price+Age+Education+US+
                     Urban+ShelveLoc+US*Advertising, Carseats)
summary(saleslm2)
## 
## Call:
## lm(formula = Sales ~ CompPrice + Income + Advertising + Population + 
##     Price + Age + Education + US + Urban + ShelveLoc + US * Advertising, 
##     data = Carseats)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.8531 -0.7140  0.0266  0.6735  3.3773 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        5.6995305  0.6023074   9.463  < 2e-16 ***
## CompPrice          0.0926214  0.0041384  22.381  < 2e-16 ***
## Income             0.0159111  0.0018414   8.641  < 2e-16 ***
## Advertising        0.2130932  0.0530297   4.018 7.04e-05 ***
## Population         0.0001540  0.0003708   0.415   0.6782    
## Price             -0.0954623  0.0026649 -35.823  < 2e-16 ***
## Age               -0.0463674  0.0031789 -14.586  < 2e-16 ***
## Education         -0.0233500  0.0197122  -1.185   0.2369    
## USYes             -0.1057320  0.1561265  -0.677   0.4987    
## UrbanYes           0.1191653  0.1127047   1.057   0.2910    
## ShelveLocGood      4.8726025  0.1532599  31.793  < 2e-16 ***
## ShelveLocMedium    1.9665296  0.1259070  15.619  < 2e-16 ***
## Advertising:USYes -0.0933384  0.0537807  -1.736   0.0834 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.016 on 387 degrees of freedom
## Multiple R-squared:  0.8744, Adjusted R-squared:  0.8705 
## F-statistic: 224.5 on 12 and 387 DF,  p-value: < 2.2e-16

Again, you can see that when the store is in the US you have to also consider the advertising budget as well. When these two variables are considered there is a slight decline in sales. What this means in practice is that advertising in the US is not as beneficial as advertising outside the US.

Below you can again see a visual of the interaction effect when the lines for US yes and no cross each other in the plot below.

ggplot(data=Carseats, aes(x=Advertising, y=Sales, group = US, colour = US)) +
        geom_smooth(method=lm,se=F)+scale_x_continuous(limits = c(0, 25))+scale_y_continuous(limits = c(0, 25))

Lastly, we will look at an interaction effect for two categorical variables.

saleslm3<-lm(Sales~CompPrice+Income+Advertising+Population+Price+Age+Education+US+
                     Urban+ShelveLoc+ShelveLoc*US, Carseats)
summary(saleslm3)
## 
## Call:
## lm(formula = Sales ~ CompPrice + Income + Advertising + Population + 
##     Price + Age + Education + US + Urban + ShelveLoc + ShelveLoc * 
##     US, data = Carseats)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.8271 -0.6839  0.0213  0.6407  3.4537 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            5.8120748  0.6089695   9.544   <2e-16 ***
## CompPrice              0.0929370  0.0041283  22.512   <2e-16 ***
## Income                 0.0158793  0.0018378   8.640   <2e-16 ***
## Advertising            0.1223281  0.0111143  11.006   <2e-16 ***
## Population             0.0001899  0.0003721   0.510   0.6100    
## Price                 -0.0952439  0.0026585 -35.826   <2e-16 ***
## Age                   -0.0459380  0.0031830 -14.433   <2e-16 ***
## Education             -0.0267021  0.0197807  -1.350   0.1778    
## USYes                 -0.3683074  0.2379400  -1.548   0.1225    
## UrbanYes               0.1438775  0.1128171   1.275   0.2030    
## ShelveLocGood          4.3491643  0.2734344  15.906   <2e-16 ***
## ShelveLocMedium        1.8967193  0.2084496   9.099   <2e-16 ***
## USYes:ShelveLocGood    0.7184116  0.3320759   2.163   0.0311 *  
## USYes:ShelveLocMedium  0.0907743  0.2631490   0.345   0.7303    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.014 on 386 degrees of freedom
## Multiple R-squared:  0.8753, Adjusted R-squared:  0.8711 
## F-statistic: 208.4 on 13 and 386 DF,  p-value: < 2.2e-16

In this case, we can see that when the store is in the US and the shelf location is good it has an effect on Sales when compared to a bad location. The plot below is a visual of this. However, it is harder to see this because the x-axis has only two categories

ggplot(data=Carseats, aes(x=US, y=Sales, group = ShelveLoc, colour = ShelveLoc)) +
        geom_smooth(method=lm,se=F)

Conclusion

Interactions effects are a great way to fine-tune a model, especially for explanatory purposes. Often, the change in r-square is not strong enough for prediction but can be used for nuanced understanding of the relationships among the variables.

Concepts to Consider for Model Development

When assessing how to conduct quantitative data analysis there are several factors to consider. In this post, we look at common either-or choices in data analysis. The concepts are as follows

  • Parametric vs Non-Parametric
  • Bias vs Variance
  • Accuracy vs Interpretability
  • Supervised vs Unsupervised
  • Regression vs Classifications

Parametric vs Non-Parametric

Parametric models make assumptions about the shape of a function. Often, the assumption is that the function is a linear in nature such as in linear regression. Making this assumption makes it much easier to estimate the actual function of a model.

Non-parametric models do not make any assumptions about the shape of the function. This allows the function to take any shape possible. Examples of non-parametric models include decision trees, support vector machines, and artificial neural networks.

The main concern with non-parametric models is they require a huge dataset when compared to parametric models.

Bias vs Variance Tradeoff

In relation to parametric and non-parametric models is the bias-variance tradeoff. A bias model is simply a model that does not listen to the data when it is tested on the test dataset. The function does what it wants as if it was not trained on the data. Parametric models tend to suffer from high bias.

Variance is the change in the function if it was estimated using new data. Variance is usually much higher in non-parametric models as they are more sensitive to the unique nature of each dataset.

Accuracy vs Interpretability 

It is also important to determine what you want to know. If your goal is accuracy then a complicated model may be more appropriate. However, if you want to infer and make conclusions about your model then it is preferred to make a simpler model that can be explained.

A model can be made simpler or more complex through the inclusion of more features or the use of a more complex algorithm. For example, regression is much easier to interpret than artificial neural networks.

Supervised vs Unsupervised

Supervised learning is learning that involves a dependent variable. Examples of supervised learning include regression, support vector machines, K nearest neighbor, and random forest.

Unsupervised learning involves a dataset that does not have a dependent variable. In this situation,  you are looking for patterns in the data. Examples of this include kmeans, principal component analysis, and cluster analysis.

Regression vs Classifications

Lastly, a problem can call for regression or classification. A regression problem involves a numeric dependent variable. A classification problem has a categorical dependent variable. Almost all models that are used for supervised machine learning can address regression or classification.

For example, regression includes numeric regression and logistic regression for classification. K nearest neighbor can do both as can random forest, support vector machines, and artificial neural networks.

Statistical Learning

Statistical learning is a discipline that focuses on understanding data. Understanding data can happen through classifying or making a numeric prediction which is called supervised learning or finding patterns in data which is called unsupervised learning,

In this post, we will examine the following

  • History of statistical learning
  • The purpose of statistical learning
  • Statistical learning vs Machine learning

History Of Statistical Learning

The early pioneers of statistical learning focused exclusively on supervised learning. Linear regression was developed in the 19th century by  Legendre and Gauss. In the 1930’s, Fisher created linear discriminant analysis. Logistic regression was created in the 1940’s as an alternative the linear discriminant analysis.

The developments of the late 19th century to the mid 20th century were limited due to the lack of computational power. However, by the 1970’s things began  to change and new algorithms emerged, specifically ones that can handle non-linear relationships

In the 1980’s Friedman and Stone developed classification and regression trees. The term generalized additive models were first used by Hastie and Tibshirani for non-linear generalized models.

Purpose of Statistical Learning

The primary goal of statistical learning is to develop a model of data you currently have to make decisions about the future. In terms of supervised learning with a numeric dependent variable, a teacher may have data on their students and want to predict future academic performance. For a categorical variable, a doctor may use data he has to predict whether someone has cancer or not. In both situations, the goal is to use what one knows to predict what one does not know.

A unique characteristic of supervised learning is that the purpose can be to predict future values or to explain the relationship between the dependent variable and another independent variable(s). Generally, data science is much more focused on prediction while the social sciences seem more concerned with explanations.

For unsupervised learning, there is no dependent variable. In terms of a practical example, a company may want to use the data they have to determine several unique categories of customers they have. Understanding large groups of customer behavior can allow the company to adjust their marketing strategy to cater to the different needs of their vast clientele.

Statistical Learning vs Machine Learning

The difference between statistical learning and machine learning is so small that for the average person it makes little difference. Generally, although some may disagree, these two terms mean essentially the same thing. Often statisticians speak of statistical learning while computer scientist speak of machine learning

Machine learning is the more popular term as it is easier to conceive of a machine learning rather than statistics learning.

Conclusion

Statistical or machine learning is a major force in the world today. With some much data and so much computing power, the possibilities are endless in terms of what kind of beneficial information can be gleaned. However, all this began with people creating a simple linear model in the 19th century.