Machine Learning, Decision Tree, Random Forest, Adaboost, Gradient Boosting.
feature selection ❌ High variance ✧ High training time ✧ Can become complex ✧ Can easily overfit Random Forest ✅ Left-out data can be used for testing ✧ High accuracy ✧ Provides feature importance estimates ✧ Can handle missing values ✧ Doesn’t require feature scaling ✧ Good performance on