Random Forest
An ensemble of many decision trees — each trained on a random subset of data and features. Majority vote for the answer.
Robust against overfitting. The classic in tabular ML before XGBoost took over. Interpretable (feature importance). Default choice for medium-difficulty classification. Implementations: scikit-learn, R's randomForest. XGBoost and LightGBM are modern successors.