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non_negative_factorization — scikit-learn 1.7.2...
array ([[ 1 , 1 ], [ 2 , 1 ], [ 3 , 1.2 ], [ 4 , 1 ], [ 5 , 0.8...n\_samples * ||vec(H)||_1\\ &+ 0.5 * alpha\_W * (1 - l1\_ratio) * n\_features...scikit-learn.org/stable/modules/generated/sklearn.decomposition.non_negative_factorization.html -
Recognizing hand-written digits — scikit-learn ...
0 0 0 1 0 0 0 0 0] [ 0 88 1 0 0 0 0 0 1 1] [ 0 0 85 1 0 0 0 0...0 0 88 1 0 0 2] [ 0 1 0 0 0 0 90 0 0 0] [ 0 0 0 0 0 1 0 88 0...scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html -
add_dummy_feature — scikit-learn 1.7.2 document...
1 ], [ 1 , 0 ]]) array([[1., 0., 1.], [1., 1., 0.]])...add_dummy_feature ( X , value = 1.0 ) [source] # Augment dataset...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.add_dummy_feature.html -
pair_confusion_matrix — scikit-learn 1.7.2 docu...
1 , 1 ], [ 1 , 1 , 0 , 0 ]) array([[8,...pair_confusion_matrix ([ 0 , 0 , 1 , 2 ], [ 0 , 0 , 1 , 1 ]) array([[8, 2],...scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html -
check_X_y — scikit-learn 1.7.2 documentation
ensure_min_samples = 1 , ensure_min_features = 1 , y_numeric = False...>>> X = [[ 1 , 2 ], [ 3 , 4 ], [ 5 , 6 ]] >>> y = [ 1 , 2 , 3 ]...scikit-learn.org/stable/modules/generated/sklearn.utils.check_X_y.html -
explained_variance_score — scikit-learn 1.7.2 d...
1 ], [ - 1 , 1 ], [ 7 , - 6 ]] >>> y_pred...cross-validation). Added in version 1.1. Returns : score float or ndarray...scikit-learn.org/stable/modules/generated/sklearn.metrics.explained_variance_score.html -
Blind source separation using FastICA — scikit-...
array ([[ 1 , 1 , 1 ], [ 0.5 , 2 , 1.0 ], [ 1.5 , 1.0 , 2.0 ]])...models , names ), 1 ): plt . subplot ( 4 , 1 , ii ) plt . title...scikit-learn.org/stable/auto_examples/decomposition/plot_ica_blind_source_separation.html -
nan_euclidean_distances — scikit-learn 1.7.2 do...
6] and [1, na, 4, 5] is: \[\sqrt{\frac{4}{2}((3-1)^2 + (6-5)^2)}\]...float ( "NaN" ) >>> X = [[ 0 , 1 ], [ 1 , nan ]] >>> nan_euclidean_distances...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.nan_euclidean_distances.html -
paired_cosine_distances — scikit-learn 1.7.2 do...
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0 ]] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.paired_cosine_distances.html -
__sklearn_is_fitted__ as Developer API — scikit...
scikit-learn 1.6 Release Highlights for scikit-learn 1.6 Metadata...__init__ ( self , parameter = 1 ): self . parameter = parameter...scikit-learn.org/stable/auto_examples/developing_estimators/sklearn_is_fitted.html