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mean_absolute_percentage_error — scikit-learn 1...
1 ], [ - 1 , 1 ], [ 7 , - 6 ]] >>> y_pred...y_true = [ 1. , 0. , 2.4 , 7. ] >>> y_pred = [ 1.2 , 0.1 , 2.4 ,...scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html -
affinity_propagation — scikit-learn 1.8.0 docum...
array ([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], ... [ 4 , 2...>>> labels array([0, 0, 0, 1, 1, 1]) Gallery examples # Visualizing...scikit-learn.org/stable/modules/generated/sklearn.cluster.affinity_propagation.html -
FeatureHasher — scikit-learn 1.8.0 documentation
-1., 0., -1., 0., 1.], [ 0., 0., 0., -1., 0., -1., 0., 0.],...0.], [ 0., -1., 0., 0., 0., 0., 0., 1.]]) fit ( X = None , y...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.FeatureHasher.html -
label_ranking_average_precision_score — scikit-...
1 ], [ 1 , 0.2 , 0.1 ]]) >>> label_ranki...y_true = np . array ([[ 1 , 0 , 0 ], [ 0 , 0 , 1 ]]) >>> y_score =...scikit-learn.org/stable/modules/generated/sklearn.metrics.label_ranking_average_precision_score.html -
make_low_rank_matrix — scikit-learn 1.8.0 docum...
profile is: ( 1 - tail_strength ) * exp ( - 1.0 * ( i / effective_rank...scikit-learn 1.3 Release Highlights for scikit-learn 1.3 Release...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_low_rank_matrix.html -
Gradient Boosting regression — scikit-learn 1.8...
subplot ( 1 , 1 , 1 ) plt . title ( "Deviance"...12 , 6 )) plt . subplot ( 1 , 2 , 1 ) plt . barh ( pos , feature_importance...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html -
ElasticNetCV — scikit-learn 1.8.0 documentation
l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1 , the penalty...(i.e. Ridge), as in [.1, .5, .7, .9, .95, .99, 1] . eps float, default=1e-3...scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNetCV.html -
NMF — scikit-learn 1.8.0 documentation
array ([[ 1 , 1 ], [ 2 , 1 ], [ 3 , 1.2 ], [ 4 , 1 ], [ 5 , 0.8...version 1.4: Added 'auto' value. Changed in version 1.6: Default...scikit-learn.org/stable/modules/generated/sklearn.decomposition.NMF.html -
compute_class_weight — scikit-learn 1.8.0 docum...
compute_class_weight >>> y = [ 1 , 1 , 1 , 1 , 0 , 0 ] >>> compute_class_weight...unique ( y ), y = y ) array([1.5 , 0.75]) On this page This Page...scikit-learn.org/stable/modules/generated/sklearn.utils.class_weight.compute_class_weight.html -
RegressorTags — scikit-learn 1.8.0 docume...
n_informative=1, bias=5.0, noise=20, random_state=42)...scikit-learn.org/stable/modules/generated/sklearn.utils.RegressorTags.html