sklearn.base
.ClassifierMixin¶
- class sklearn.base.ClassifierMixin[source]¶
Mixin class for all classifiers in scikit-learn.
This mixin defines the following functionality:
_estimator_type
class attribute defaulting to"classifier"
;score
method that default toaccuracy_score
.enforce that
fit
requiresy
to be passed through therequires_y
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...
Methods
score
(X, y[, sample_weight])Return the mean accuracy on the given test data and labels.
- 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
.
Examples using sklearn.base.ClassifierMixin
¶
__sklearn_is_fitted__ as Developer API