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accuracy_score — scikit-learn 1.8.0 docum...
1 ], [ 1 , 1 ]]), np . ones (( 2 , 2...= [ 0 , 2 , 1 , 3 ] >>> y_true = [ 0 , 1 , 2 , 3 ] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html -
inplace_csr_row_normalize_l1 — scikit-lea...
1 , 2 , 3 ]) >>> data = np . array ([ 1.0 , 2.0...0. ], [0. , 0. , 1. , 0. ], [0. , 0. , 0. , 1. ]]) On this page...scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normaliz... -
compute_optics_graph — scikit-learn 1.8.0...
1.41, 1.41, 1. , 1. , 4.12]) >>>...min_samples int > 1 or float between 0 and 1 The number of samples...scikit-learn.org/stable/modules/generated/sklearn.cluster.compute_optics_graph.html -
cluster_optics_xi — scikit-learn 1.8.0 do...
1, 1, 1]) >>> clusters array([[0,...min_samples int > 1 or float between 0 and 1 The same as the min_samples...scikit-learn.org/stable/modules/generated/sklearn.cluster.cluster_optics_xi.html -
ClusterMixin — scikit-learn 1.8.0 documen...
fit_predict ( X ) array([1, 1, 1]) fit_predict ( X , y = None...return self >>> X = [[ 1 , 2 ], [ 2 , 3 ], [ 3 , 4 ]] >>>...scikit-learn.org/stable/modules/generated/sklearn.base.ClusterMixin.html -
IncrementalPCA — scikit-learn 1.8.0 docum...
([[ - 1 , - 1 ], [ - 2 , - 1 ], [ - 3 , - 2 ], ... [ 1 , 1 ], [...principal components, versus 1 large SVD of complexity O(n_samples...scikit-learn.org/stable/modules/generated/sklearn.decomposition.IncrementalPCA.html -
ExtraTreesRegressor — scikit-learn 1.8.0 ...
min_samples_leaf = 1 , min_weight_fraction_leaf = 0.0 , max_features = 1.0 ,...e.g. 0.3. Changed in version 1.1: The default of max_features...scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html -
make_sparse_uncorrelated — scikit-learn 1...
described in Celeux et al [1]. as: X ~ N ( 0 , 1 ) y ( X ) = X [:, 0...0 ] + 2 * X [:, 1 ] - 2 * X [:, 2 ] - 1.5 * X [:, 3 ] Only the...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_sparse_uncorrelated.html -
SparseCoder — scikit-learn 1.8.0 document...
1 , 0 ], ... [ - 1 , - 1 , 2 ], ... [ 1 , 1 , 1 ], ......>>> X = np . array ([[ - 1 , - 1 , - 1 ], [ 0 , 0 , 3 ]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.decomposition.SparseCoder.html -
VotingClassifier — scikit-learn 1.8.0 doc...
([[ - 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.ensemble.VotingClassifier.html