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  1. clear_data_home — scikit-learn 1.5.2 documentation

    Skip to main content Back to top Ctrl + K GitHub clear_data_home # sklearn.datasets. clear_data_home ( data_home = No...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.clear_data_home.html
    Sat Nov 23 04:49:16 UTC 2024
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  2. sklearn.cross_decomposition — scikit-learn 1.5....

    Algorithms for cross decomposition. User guide. See the Cross decomposition section for further details.
    scikit-learn.org/stable/api/sklearn.cross_decomposition.html
    Sat Nov 23 04:49:16 UTC 2024
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  3. 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
    Sat Nov 23 04:49:15 UTC 2024
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  4. cluster_optics_dbscan — scikit-learn 1.5.2 docu...

    Gallery examples: Demo of OPTICS clustering algorithm
    scikit-learn.org/stable/modules/generated/sklearn.cluster.cluster_optics_dbscan.html
    Sat Nov 23 04:49:15 UTC 2024
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  5. 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
    Sat Nov 23 04:49:14 UTC 2024
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  6. make_spd_matrix — scikit-learn 1.5.2 documentation

    Skip to main content Back to top Ctrl + K GitHub make_spd_matrix # sklearn.datasets. make_spd_matrix ( n_dim , * , ra...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.make_spd_matrix.html
    Sat Nov 23 04:49:15 UTC 2024
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  7. sklearn.discriminant_analysis — scikit-learn 1....

    Linear and quadratic discriminant analysis. User guide. See the Linear and Quadratic Discriminant Analysis section for further details.
    scikit-learn.org/stable/api/sklearn.discriminant_analysis.html
    Sat Nov 23 04:49:15 UTC 2024
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  8. L1 Penalty and Sparsity in Logistic Regression ...

    Comparison of the sparsity (percentage of zero coefficients) of solutions when L1, L2 and Elastic-Net penalty are used for different values of C. We can see that large values of C give more freedom...
    scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.html
    Sat Nov 23 04:49:14 UTC 2024
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  9. Non-negative least squares — scikit-learn 1.5.2...

    In this example, we fit a linear model with positive constraints on the regression coefficients and compare the estimated coefficients to a classic linear regression. Generate some random data Spli...
    scikit-learn.org/stable/auto_examples/linear_model/plot_nnls.html
    Sat Nov 23 04:49:15 UTC 2024
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  10. make_column_selector — scikit-learn 1.5.2 docum...

    Gallery examples: Categorical Feature Support in Gradient Boosting Combine predictors using stacking Evaluation of outlier detection estimators Column Transformer with Mixed Types
    scikit-learn.org/stable/modules/generated/sklearn.compose.make_column_selector.html
    Sat Nov 23 04:49:15 UTC 2024
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