OutlierMixin (original) (raw)

class sklearn.base.OutlierMixin[source]#

Mixin class for all outlier detection estimators in scikit-learn.

This mixin defines the following functionality:

• set estimator type to "outlier_detector" through the estimator_type tag;
• fit_predict method that default to fit and predict.

Examples

import numpy as np from sklearn.base import BaseEstimator, OutlierMixin class MyEstimator(OutlierMixin): ... def fit(self, X, y=None): ... self.is_fitted_ = True ... return self ... def predict(self, X): ... return np.ones(shape=len(X)) estimator = MyEstimator() X = np.array([[1, 2], [2, 3], [3, 4]]) estimator.fit_predict(X) array([1., 1., 1.])

fit_predict(X, y=None, **kwargs)[source]#

Perform fit on X and returns labels for X.

Returns -1 for outliers and 1 for inliers.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

The input samples.

yIgnored

Not used, present for API consistency by convention.

**kwargsdict

Arguments to be passed to fit.

Added in version 1.4.

Returns:

yndarray of shape (n_samples,)

1 for inliers, -1 for outliers.