Chapter 5. Decision Trees and Random Forests

Note

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

Note

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