stency with other outlier detection algorithms.

    .. versionadded:: 0.20

shape_fit_ : tuple of int of shape (n_dimensions_of_X,)
    Array dimensions of training vector ``X``.

support_ : ndarray of shape (n_SV,)
    Indices of support vectors.

support_vectors_ : ndarray of shape (n_SV, n_features)
    Support vectors.

See Also
--------
sklearn.linear_model.SGDOneClassSVM : Solves linear One-Class SVM using
    Stochastic Gradient Descent.
sklearn.neighbors.LocalOutlierFactor : Unsupervised Outlier Detection using
    Local Outlier Factor (LOF).
sklearn.ensemble.IsolationForest : Isolation Forest Algorithm.

Examples
--------
>>> from sklearn.svm import OneClassSVM
>>> X = [[0], [0.44], [0.45], [0.46], [1]]
>>> clf = OneClassSVM(gamma='auto').fit(X)
>>> clf.predict(X)
array([-1,  1,  1,  1, -1])
>>> clf.score_samples(X)
array([1.7798..., 2.0547..., 2.0556..., 2.0561..., 1.7332...])

For a more extended example,
see :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py`
Ú	one_classr: