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Demonstrating the different strategies of KBins...
]]) centers_1 = np . array ([[ 0 , 0 ], [ 3 , 1 ]]) # construct...strategies ) + 1 , i ) ax . scatter ( X [:, 0 ], X [:, 1 ], edgecolors...scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_strategies.html -
resample — scikit-learn 1.6.0 documentation
1 , 1 , 1 , 1 , 1 ] >>> resample ( y , n_samples = 5 , replace...array([[1., 0.], [2., 1.], [1., 0.]]) >>> y array([0, 1, 0]) >>>...scikit-learn.org/stable/modules/generated/sklearn.utils.resample.html -
Quantile regression — scikit-learn 1.6.0 docume...
scikit-learn 1.1 Release Highlights for scikit-learn 1.1 Lagged features...axs [ 1 , 0 ] . set_xlabel ( "Residuals" ) _ = axs [ 1 , 1 ] ....scikit-learn.org/stable/auto_examples/linear_model/plot_quantile_regression.html -
Visualizing the stock market structure — scikit...
index ] = 1 dy = y - embedding [ 1 ] dy [ index ] = 1 this_dx =...alphas = np . logspace ( - 1.5 , 1 , num = 10 ) edge_model = covariance...scikit-learn.org/stable/auto_examples/applications/plot_stock_market.html -
is_multilabel — scikit-learn 1.6.0 documentation
1 , 0 , 1 ]) False >>> is_multilabel ([[ 1 ], [ 0 ,...is_multilabel ( np . array ([[ 1 , 0 ], [ 0 , 0 ]])) True >>> is_multilabel...scikit-learn.org/stable/modules/generated/sklearn.utils.multiclass.is_multilabel.html -
PredefinedSplit — scikit-learn 1.6.0 documentation
1 , 1 ]) >>> test_fold = [ 0 , 1 , - 1 , 1 ] >>> ps...PredefinedSplit(test_fold=array([ 0, 1, -1, 1])) >>> for i , ( train_index...scikit-learn.org/stable/modules/generated/sklearn.model_selection.PredefinedSplit.html -
MultiTaskLassoCV — scikit-learn 1.6.0 documenta...
means 1 unless in a joblib.parallel_backend context. -1 means...alpha * || w || _1 For multi-output tasks it is: ( 1 / ( 2 * n_samples...scikit-learn.org/stable/modules/generated/sklearn.linear_model.MultiTaskLassoCV.html -
Getting Started — scikit-learn 1.6.0 documentation
dataset is easy array([1., 1., 1., 1., 1.]) Automatic parameter...transform ( X ) array([[-1., 1.], [ 1., -1.]]) Sometimes, you want...scikit-learn.org/stable/getting_started.html -
LeavePGroupsOut — scikit-learn 1.6.0 documentation
Test: index=[0 1], group=[1 2] Fold 1: Train: index=[1], group=[2]...array ([ 1 , 2 , 1 ]) >>> groups = np . array ([ 1 , 2 , 3 ])...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeavePGroupsOut.html -
Agglomerative clustering with and without struc...
) t = 1.5 * np . pi * ( 1 + 3 * np . random . rand ( 1 , n_samples..."single" )): plt . subplot ( 1 , 4 , index + 1 ) model = AgglomerativeCluster...scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering.html