Decision Trees and Ensembles: From Splits to Random Forests and XGBoost
A complete guide to decision trees, covering entropy, information gain, one-hot encoding, regression trees, and ensemble methods like Random Forest and XGBoost.
9 min read
4. Decision trees
Decision trees
Learning Process
Decision tree learning
Measuring purity
Choosing a split: Information Gain
Putting it together
Using one-hot encoding of categorical features
Continuous valued features
Regression Trees
Tree ensembles
Using multiple decision trees
Sampling with replacement
Random forest algorithm
XGBoost
When to use decision trees
