Random Forests

Summary

An ensemble of many decision trees trained on random subsets of data/features, averaged together — more accurate and robust than any single tree.

• 1Train a Random Forest and compare its accuracy to a single decision tree.
• 2Learn what 'bagging' and feature randomness contribute to reducing overfitting.
• 3Use feature importance from a Random Forest to interpret which inputs matter most.