ClassifierMixin (original) (raw)

class sklearn.base.ClassifierMixin[source]#

Mixin class for all classifiers in scikit-learn.

This mixin defines the following functionality:

• set estimator type to "classifier" through the estimator_type tag;
• score method that default to accuracy_score.
• enforce that fit requires y to be passed through the requires_y tag, which is done by setting the classifier type tag.

Read more in the User Guide.

Examples

import numpy as np from sklearn.base import BaseEstimator, ClassifierMixin

Mixin classes should always be on the left-hand side for a correct MRO

class MyEstimator(ClassifierMixin, BaseEstimator): ... def init(self, *, param=1): ... self.param = param ... def fit(self, X, y=None): ... self.is_fitted_ = True ... return self ... def predict(self, X): ... return np.full(shape=X.shape[0], fill_value=self.param) estimator = MyEstimator(param=1) X = np.array([[1, 2], [2, 3], [3, 4]]) y = np.array([1, 0, 1]) estimator.fit(X, y).predict(X) array([1, 1, 1]) estimator.score(X, y) 0.66...

score(X, y, sample_weight=None)[source]#

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

scorefloat

Mean accuracy of self.predict(X) w.r.t. y.

Gallery examples#