Statistics Every Programmer Needs

6 Fitting a decision tree and a random forest

This chapter covers

• Decision trees and random forests
• Model interpretation and evaluation
• Mathematical foundations
• Data exploration through grouped bar charts and histograms
• Common data wrangling techniques

In the previous chapter, we solved a classification problem using logistic regression, achieving 87% accuracy in predicting the variety of Turkish raisins based on their morphological features. In this chapter, we will approach a similar classification problem using two powerful modeling techniques: decision trees and random forests.

A decision tree is a simple, intuitive model that makes decisions by recursively splitting the data into subsets based on the most significant feature at ...