Machine Learning, Decision Tree, Random Forest, Adaboost, Gradient Boosting.
Good performance ❌ Harder to tune hyperparameters XGBoost ✅ Less feature engineering required ✧ Outliers have little impact ✧ Can output feature importance ✧ Handles large datasets ✧ Good model performance ✧ Less prone to overfitting ❌ Difficult to interpret ✧ Harder to tune as there are numerous