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hamming_loss — scikit-learn 1.8.0 documen...
1 ], [ 1 , 1 ]]), np . zeros (( 2 , 2...between 0 and 1, lower being better. References [ 1 ] Grigorios...scikit-learn.org/stable/modules/generated/sklearn.metrics.hamming_loss.html -
EllipticEnvelope — scikit-learn 1.8.0 doc...
n_features + 1) / 2 * n_samples . Range is (0, 1). contamination...>>> # predict returns 1 for an inlier and -1 for an outlier >>>...scikit-learn.org/stable/modules/generated/sklearn.covariance.EllipticEnvelope.html -
OneToOneFeatureMixin — scikit-learn 1.8.0...
This mixin assumes there’s a 1-to-1 correspondence between input...shape [ 1 ] ... return self >>> X = np . array ([[ 1 , 2...scikit-learn.org/stable/modules/generated/sklearn.base.OneToOneFeatureMixin.html -
oas — scikit-learn 1.8.0 documentation
References [ 1 ] ( 1 , 2 ) “Shrinkage algorithms...The regularised covariance is: (1 - shrinkage) * cov + shrinkage...scikit-learn.org/stable/modules/generated/oas-function.html -
KFold — scikit-learn 1.8.0 documentation
3] Test: index=[0 1] Fold 1: Train: index=[0 1] Test: index=[2...X = np . array ([[ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ]])...scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html -
adjusted_rand_score — scikit-learn 1.8.0 ...
1 , 1 ], [ 0 , 0 , 1 , 1 ]) 1.0 >>> adjusted_rand_score...adjusted_rand_score ([ 0 , 0 , 1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 Labelings that...scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html -
KDTree — scikit-learn 1.8.0 documentation
query ( X [: 1 ], k = 3 ) >>> print...indices of 3 closest neighbors [0 3 1] >>> print ( dist ) #...scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html -
manhattan_distances — scikit-learn 1.8.0 ...
array([[1.]]) >>> manhattan_distances ([[ 1 , 2 ], [...manhattan_distances ([[ 3 ]], [[ 2 ]]) array([[1.]]) >>> manhattan_distances...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.manhattan_distances.html -
NearestCentroid — scikit-learn 1.8.0 docu...
([[ - 1 , - 1 ], [ - 2 , - 1 ], [ - 3 , - 2 ], [ 1 , 1 ], [ 2...2 , 1 ], [ 3 , 2 ]]) >>> y = np . array ([ 1 , 1 , 1...scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestCentroid.html -
make_classification — scikit-learn 1.8.0 ...
class_sep = 1.0 , hypercube = True , shift = 0.0 , scale = 1.0 , shuffle...[np.int64(0), np.int64(0), np.int64(1), np.int64(1), np.int64(0)] Gallery...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html