Data Science Projects with Python

Chapter 5:

Decision Trees and Random Forests

Learning Objectives

By the end of this chapter, you will be able to:

• Train a decision tree model in scikit-learn
• Use Graphviz to visualize a trained decision tree model
• Formulate the cost functions used to split nodes in a decision tree
• Perform a hyperparameter grid search using cross-validation with scikit-learn functions
• Train a random forest model in scikit-learn
• Evaluate the most important features in a random forest model

This chapter introduces decision trees and random forests in scikit-learn in addition to describing the method to perform hyperparameter grid search.

Introduction

In the last two chapters, we have gained a thorough understanding of the workings of logistic regression. ...