Decision trees are yet another method of machine learning that is used for classifying outcomes. Decision trees are very useful for, as you can guess, making decisions based on the characteristics of the data.
In this post, we will discuss the following
- Physical traits of decision trees
- How decision trees work
- Pros and cons of decision trees
Physical Traits of a Decision Tree
Decision trees consist of what is called a tree structure. The tree structure consists of a root node, decision nodes, branches and leaf nodes.
A root node is an initial decision made in the tree. This depends on which feature the algorithm selects first.
Following the root node, the tree splits into various branches. Each branch leads to an additional decision node where the data is further subdivided. When you reach the bottom of a tree at the terminal node(s) these are also called leaf nodes.
How Decision Trees Work
Decision trees use a heuristic called recursive partitioning. What this does is it splits the overall dataset into smaller and smaller subsets until each subset is as close to pure (having the same characteristics) as possible. This process is also known as divide and conquer.
The mathematics for deciding how to split the data is based on an equation called entropy, which measures the purity of a potential decision node. The lower the entropy scores the purer the decision node is. The entropy can range from 0 (most pure) to 1 (most impure).
One of the most popular algorithms for developing decision trees is the C5.0 algorithm. This algorithm, in particular, uses entropy to assess potential decision nodes.
Pros and Cons
The prose of decision trees includes its versatile nature. Decision trees can deal with all types of data as well as missing data. Furthermore, this approach learns automatically and only uses the most important features. Lastly, a deep understanding of mathematics is not necessary to use this method in comparison to more complex models.
Some problems with decision trees are that they can easily overfit the data. This means that the tree does not generalize well to other datasets. In addition, a large complex tree can be hard to interpret, which may be yet another indication of overfitting.
Decision trees provide another vehicle that researchers can use to empower decision making. This model is most useful particularly when a decision that was made needs to be explained and defended. For example, when rejecting a person’s loan application. Complex models made provide stronger mathematical reasons but would be difficult to explain to an irate customer.
Therefore, for complex calculation presented in an easy to follow format. Decision trees are one possibility.