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  1. Comparison of F-test and mutual information — s...

    follows: y = x_1 + sin(6 * pi * x_2) + 0.1 * N(0, 1), that is the...features x_1, x_2, x_3 distributed uniformly over [0, 1], the target...
    scikit-learn.org/stable/auto_examples/feature_selection/plot_f_test_vs_mi.html
    Sat Aug 02 00:15:37 UTC 2025
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  2. 11. Common pitfalls and recommended practices —...

    n_features = 1 , noise = 1 ) >>> X_train , X_test ,...applies to using None . 11.3.1.1. Estimators # Passing instances...
    scikit-learn.org/stable/common_pitfalls.html
    Sat Aug 02 00:15:36 UTC 2025
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  3. Classification of text documents using sparse f...

    the mean squared error on {-1, 1} encoded targets, one for each...n_features: { X_train . shape [ 1 ] } " ) print ( f "vectorize testing...
    scikit-learn.org/stable/auto_examples/text/plot_document_classification_20newsgroups.html
    Sat Aug 02 00:15:37 UTC 2025
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  4. Comparing different clustering algorithms on to...

    "connectivity matrix is [0-9]{1,2}" " > 1. Completing it to avoid stopping...= n_samples , cluster_std = [ 1.0 , 2.5 , 0.5 ], random_state...
    scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html
    Sat Aug 02 00:15:37 UTC 2025
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  5. Face completion with a multi-output estimators ...

    shape [ 1 ] # Upper half of the faces X_train...X_train = train [:, : ( n_pixels + 1 ) // 2 ] # Lower half of the faces...
    scikit-learn.org/stable/auto_examples/miscellaneous/plot_multioutput_face_completion.html
    Sat Aug 02 00:15:37 UTC 2025
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  6. ClassNamePrefixFeaturesOutMixin — scikit-learn ...

    shape [ 1 ] ... return self >>> X = np . array ([[ 1 , 2 ], [...
    scikit-learn.org/stable/modules/generated/sklearn.base.ClassNamePrefixFeaturesOutMixin.html
    Sat Aug 02 00:15:35 UTC 2025
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  7. Faces recognition example using eigenfaces and ...

    1 )[ - 1 ] true_name = target_names...y_test [ i ]] . rsplit ( " " , 1 )[ - 1 ] return "predicted: %s \n...
    scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html
    Sat Aug 02 00:15:37 UTC 2025
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  8. SVM: Separating hyperplane for unbalanced class...

    clusters of random points n_samples_1 = 1000 n_samples_2 = 100 centers...2.0 , 2.0 ]] clusters_std = [ 1.5 , 0.5 ] X , y = make_blobs (...
    scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane_unbalanced.html
    Sat Aug 02 00:15:35 UTC 2025
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  9. Tags — scikit-learn 1.7.1 documentation

    scikit-learn 1.6 Release Highlights for scikit-learn 1.6 On this...
    scikit-learn.org/stable/modules/generated/sklearn.utils.Tags.html
    Sat Aug 02 00:15:36 UTC 2025
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  10. SGD: Maximum margin separating hyperplane — sci...

    linspace ( - 1 , 5 , 10 ) yy = np . linspace ( - 1 , 5 , 10 ) X1...= p [ 0 ] levels = [ - 1.0 , 0.0 , 1.0 ] linestyles = [ "dashed"...
    scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_separating_hyperplane.html
    Sat Aug 02 00:15:37 UTC 2025
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