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ndcg_score — scikit-learn 1.7.1 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 -
chi2_kernel — scikit-learn 1.7.1 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 -
linear_kernel — scikit-learn 1.7.1 documentation
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0 ]] >>>...linear_kernel ( X , Y ) array([[0., 0.], [1., 2.]]) On this page This Page...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.linear_kernel.html -
k_means — scikit-learn 1.7.1 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 -
fbeta_score — scikit-learn 1.7.1 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 -
config_context — scikit-learn 1.7.1 documentation
Added in version 1.1. enable_cython_pairwise_dist...configuration setting. Added in version 1.1. array_api_dispatch bool, default=None...scikit-learn.org/stable/modules/generated/sklearn.config_context.html -
load_sample_image — scikit-learn 1.7.1 document...
Skip to main content Back to top Ctrl + K GitHub Choose version load_sample_image # sklearn.datasets. load_sample_ima...scikit-learn.org/stable/modules/generated/sklearn.datasets.load_sample_image.html -
sklearn.kernel_approximation — scikit-learn 1.7...
Approximate kernel feature maps based on Fourier transforms and count sketches. User guide. See the Kernel Approximation section for further details.scikit-learn.org/stable/api/sklearn.kernel_approximation.html -
sklearn.naive_bayes — scikit-learn 1.7.1 docume...
Naive Bayes algorithms. These are supervised learning methods based on applying Bayes’ theorem with strong (naive) feature independence assumptions. User guide. See the Naive Bayes section for furt...scikit-learn.org/stable/api/sklearn.naive_bayes.html