Decision Trees and Random Forests
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.
In the last two chapters, we have gained a thorough understanding of the workings of logistic regression. ...