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normalize — scikit-learn 1.7.2 documentation
import normalize >>> X = [[ - 2 , 1 , 2 ], [ - 1 , 0 , 1 ]] >>> normalize...independently array([[-0.4, 0.2, 0.4], [-0.5, 0. , 0.5]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html -
LeaveOneOut — scikit-learn 1.7.2 documentation
2 ], [ 3 , 4 ]]) >>> y = np . array ([ 1 , 2 ]) >>> loo...>>> loo . get_n_splits ( X ) 2 >>> print ( loo ) LeaveOneOut()...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeaveOneOut.html -
LabelEncoder — scikit-learn 1.7.2 documentation
array([2, 2, 1]...) >>> list ( le . inverse_transform ([ 2 , 2 ,...LabelEncoder () >>> le . fit ([ 1 , 2 , 2 , 6 ]) LabelEncoder() >>> le...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html -
EllipticEnvelope — scikit-learn 1.7.2 documenta...
(n_samples + n_features + 1) / 2 * n_samples . Range is (0, 1)....with n_samples > n_features ** 2 . References [ 1 ] Rousseeuw,...scikit-learn.org/stable/modules/generated/sklearn.covariance.EllipticEnvelope.html -
2.6. Covariance estimation — scikit-learn 1.7.2...
2.6.2. Shrunk Covariance # 2.6.2.1. Basic shrinkage...ShrunkCovariance object to data. 2.6.2.2. Ledoit-Wolf shrinkage # In...scikit-learn.org/stable/modules/covariance.html -
dbscan — scikit-learn 1.7.2 documentation
2 ], [ 2 , 2 ], [ 2 , 3 ], [ 8 , 7 ], [ 8...min_samples = 2 ) >>> core_samples array([0, 1, 2, 3, 4]) >>> labels...scikit-learn.org/stable/modules/generated/dbscan-function.html -
ClusterMixin — scikit-learn 1.7.2 documentation
2 ], [ 2 , 3 ], [ 3 , 4 ]] >>> MyClusterer...scikit-learn.org/stable/modules/generated/sklearn.base.ClusterMixin.html -
OutlierMixin — scikit-learn 1.7.2 documentation
2 ], [ 2 , 3 ], [ 3 , 4 ]]) >>> estimator...scikit-learn.org/stable/modules/generated/sklearn.base.OutlierMixin.html -
enet_path — scikit-learn 1.7.2 documentation
it is: 1 / ( 2 * n_samples ) * || y - Xw ||^ 2_2 + alpha * l1_ratio...|| w ||^ 2_2 For multi-output tasks it is: ( 1 / ( 2 * n_samples...scikit-learn.org/stable/modules/generated/sklearn.linear_model.enet_path.html -
make_classification — scikit-learn 1.7.2 docume...
n_informative = 2 , n_redundant = 2 , n_repeated = 0 , n_classes = 2 , n_clusters_per_class...n_clusters_per_class = 2 , weights = None , flip_y = 0.01 , class_sep...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html