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  1. Scalable learning with polynomial kernel approx...

    This example illustrates the use of PolynomialCountSketch to efficiently generate polynomial kernel feature-space approximations. This is used to train linear classifiers that approximate the accur...
    scikit-learn.org/stable/auto_examples/kernel_approximation/plot_scalable_poly_kernels.html
    Fri Aug 22 18:00:29 UTC 2025
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  2. Using KBinsDiscretizer to discretize continuous...

    The example compares prediction result of linear regression (linear model) and decision tree (tree based model) with and without discretization of real-valued features. As is shown in the result be...
    scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization.html
    Fri Aug 22 18:00:32 UTC 2025
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  3. Dimensionality Reduction with Neighborhood Comp...

    Sample usage of Neighborhood Components Analysis for dimensionality reduction. This example compares different (linear) dimensionality reduction methods applied on the Digits data set. The data set...
    scikit-learn.org/stable/auto_examples/neighbors/plot_nca_dim_reduction.html
    Fri Aug 22 18:00:32 UTC 2025
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  4. make_spd_matrix — scikit-learn 1.7.1 documentation

    Skip to main content Back to top Ctrl + K GitHub Choose version make_spd_matrix # sklearn.datasets. make_spd_matrix (...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.make_spd_matrix.html
    Fri Aug 22 18:00:33 UTC 2025
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  5. dump_svmlight_file — scikit-learn 1.7.1 documen...

    Skip to main content Back to top Ctrl + K GitHub Choose version dump_svmlight_file # sklearn.datasets. dump_svmlight_...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.dump_svmlight_file.html
    Fri Aug 22 18:00:34 UTC 2025
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  6. sklearn.gaussian_process — scikit-learn 1.7.1 d...

    Gaussian process based regression and classification. User guide. See the Gaussian Processes section for further details. Kernels: A set of kernels that can be combined by operators and used in Gau...
    scikit-learn.org/stable/api/sklearn.gaussian_process.html
    Fri Aug 22 18:00:32 UTC 2025
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  7. get_scorer_names — scikit-learn 1.7.1 documenta...

    Skip to main content Back to top Ctrl + K GitHub Choose version get_scorer_names # sklearn.metrics. get_scorer_names ...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.get_scorer_names.html
    Fri Aug 22 18:00:33 UTC 2025
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  8. fetch_lfw_pairs — scikit-learn 1.7.1 documentation

    Skip to main content Back to top Ctrl + K GitHub Choose version fetch_lfw_pairs # sklearn.datasets. fetch_lfw_pairs (...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_lfw_pairs.html
    Fri Aug 22 18:00:34 UTC 2025
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  9. clear_data_home — scikit-learn 1.7.1 documentation

    Skip to main content Back to top Ctrl + K GitHub Choose version clear_data_home # sklearn.datasets. clear_data_home (...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.clear_data_home.html
    Fri Aug 22 18:00:34 UTC 2025
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  10. make_column_selector — scikit-learn 1.7.1 docum...

    Gallery examples: Column Transformer with Mixed Types Categorical Feature Support in Gradient Boosting Combine predictors using stacking Evaluation of outlier detection estimators
    scikit-learn.org/stable/modules/generated/sklearn.compose.make_column_selector.html
    Fri Aug 22 18:00:33 UTC 2025
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