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.
