sklearn.base
.OutlierMixin#
- class sklearn.base.OutlierMixin[source]#
Mixin class for all outlier detection estimators in scikit-learn.
This mixin defines the following functionality:
_estimator_type
class attribute defaulting tooutlier_detector
;fit_predict
method that default tofit
andpredict
.
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.])
Methods
fit_predict
(X[, y])Perform fit on X and returns labels for X.
- 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
.New in version 1.4.
- Returns:
- yndarray of shape (n_samples,)
1 for inliers, -1 for outliers.