scikit-learn

| Logistic Regression | Baseline, interpretable | Fast, probabilistic | | Random Forest | General purpose | Handles non-linear, feature importance | | Gradient Boosting | Best accuracy | State-of-art for tabular | | SVM | High-dimensional data | Works well with few samples | | KNN | Simple problems | No training, instance-based |

| Linear Regression | Baseline | Interpretable coefficients | | Ridge/Lasso | Regularization needed | L2 vs L1 penalty | | Random Forest | Non-linear relationships | Robust to outliers | | Gradient Boosting | Best accuracy | XGBoost, LightGBM wrappers |

| KMeans | Spherical clusters | nclusters (must specify) | | DBSCAN | Arbitrary shapes | eps (density) | | Agglomerative | Hierarchical | nclusters or distance threshold | | Gaussian Mixture | Soft clustering | ncomponents |