Classification Algorithms in Python

Classification with Scikit-learn

Scikit-learn provides various classification algorithms with unified interface.

Logistic Regression

LogisticRegression implements logistic regression. fit(X, y) trains. predict(X) predicts classes. predict_proba(X) gives probabilities.

Regularization (C parameter) controls complexity. Multi-class: 'ovr' or 'multinomial'.

Decision Trees

DecisionTreeClassifier creates tree-based classifiers. max_depth limits tree depth. min_samples_split requires minimum samples to split.

Trees are interpretable: feature_importances_ shows feature contributions.

Support Vector Machines

SVC implements SVM. kernel='rbf' for non-linear boundaries. C controls regularization. gamma controls RBF width.

SVMs are powerful but can be slow on large datasets.

Key Takeaways

1. Scikit-learn provides many classification algorithms
2. Logistic regression handles multi-class well
3. Decision trees are interpretable; SVMs are powerful