A recap on Scikit-learn's estimator interface

X : data, 2d numpy array or scipy sparse matrix of shape (n_samples, n_features)

y : targets, 1d numpy array of shape (n_samples,)

Methods

Efficient alternatives, methods for models that don't generalize

model.fit_predict(X) (clustering)

model.fit_transform(X) (manifold learning)

Additional methods

Model evaluation : score(X, [y])

Uncertainties from Classifiers: decision_function(X) and predict_proba(X).

Attributes

Classifiers: classes_

Clustering: labels_

Manifold Learning: embedding_

Linear models: coef_

Linear Decompositions: components_