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ndcg_score — scikit-learn 1.6.0 documentation
1.1 , 1. , .5 , .0 ]]) >>> ndcg_score...scores = np . asarray ([[ 1 , 0 , 0 , 0 , 1 ]]) >>> # by default ties...scikit-learn.org/stable/modules/generated/sklearn.metrics.ndcg_score.html -
Probability calibration of classifiers — scikit...
51 )[ 1 :: 2 ], y_test [ order ] . reshape ( 25 , - 1 ) . mean...] = 0 y [ n_samples // 2 :] = 1 sample_weight = np . random ....scikit-learn.org/stable/auto_examples/calibration/plot_calibration.html -
inplace_swap_column — scikit-learn 1.6.0 docume...
1 ) >>> csr . todense () matrix([[0,...scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs.inplace_swap_column.html -
4. Inspection — scikit-learn 1.6.0 documentation
1.1. Partial dependence plots 4.1.2. Individual...expectation (ICE) plot 4.1.3. Mathematical Definition 4.1.4. Computation...scikit-learn.org/stable/inspection.html -
chi2_kernel — scikit-learn 1.6.0 documentation
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0 ]] >>>...chi2_kernel ( X , Y = None , gamma = 1.0 ) [source] # Compute the exponential...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.chi2_kernel.html -
partial_dependence — scikit-learn 1.6.0 documen...
[ 1 , 0 , 0 ]] >>> y = [ 0 , 1 ] >>> from sklearn.ensemble...interacting features (e.g. [(0, 1)] ) for which the partial dependency...scikit-learn.org/stable/modules/generated/sklearn.inspection.partial_dependence.html -
fbeta_score — scikit-learn 1.6.0 documentation
1 , 2 , 0 , 1 , 2 ] >>> y_pred = [ 0 , 2 , 1 , 0 , 0...beta > 1 gives more weight to recall, while beta < 1 favors precision....scikit-learn.org/stable/modules/generated/sklearn.metrics.fbeta_score.html -
k_means — scikit-learn 1.6.0 documentation
[ 1., 2.]]) >>> label array([1, 1, 1, 0, 0, 0], dtype=int32)...X = np . array ([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], ... [ 10 , 2...scikit-learn.org/stable/modules/generated/sklearn.cluster.k_means.html -
linear_kernel — scikit-learn 1.6.0 documentation
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0 ]] >>>...linear_kernel ( X , Y ) array([[0., 0.], [1., 2.]]) On this page Show Source...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.linear_kernel.html -
check_estimator — scikit-learn 1.7.dev0 documen...
since version 1.6: generate_only will be removed in 1.8. Use est...since version 1.6: generate_only will be removed in 1.8. Use est...scikit-learn.org/dev/modules/generated/sklearn.utils.estimator_checks.check_estimator.html