Lessons
1
Decision trees: the splitting problem
2
Information gain and Gini impurity
3
Overfitting in trees: pruning
4
Random forests: bagging and feature subsampling
5
Gradient boosting: additive training
6
XGBoost: the second-order approximation
7
When trees beat neural networks