- Decision trees learn if-then-else rules from data.
- Finding the best split: select the least impure split. This algorithm can overfit, that's why we control it by limiting the max depth and the size of the group.
- Random forest is a way of combining multiple decision trees. It should have a diverse set of models to make good predictions.
- Gradient boosting trains model sequentially: each model tries to fix errors of the previous model. XGBoost is an implementation of gradient boosting.
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