Summary: Decision Trees and Random Forests
Explore how decision trees and random forests enhance predictive modeling by managing non-linear relationships and complex feature interactions. Understand the reduction of overfitting through random forest ensembles and prepare for learning gradient boosting and SHAP value explanations in upcoming lessons.
We'll cover the following...
We'll cover the following...
In this chapter, we’ve learned how to use decision trees and the ensemble models called random forests that are made up of many decision trees. Using these simply conceived models, we were able to make better predictions than we could with logistic regression, judging by the cross-validation ROC AUC score. This is often the case for many real-world problems. ...
