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  1. Gaussian process classification (GPC) on iris d...

    y ) kernel = 1.0 * RBF ([ 1.0 , 1.0 ]) gpc_rbf_anisotropic...() - 1 , X [:, 0 ] . max () + 1 y_min , y_max = X [:, 1 ] . min...
    scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_iris.html
    Mon Mar 23 20:39:20 UTC 2026
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  2. Release Highlights for scikit-learn 0.24 — scik...

    default=-1 Hard limit on iterations within solver, or -1 for no...been removed in version 1.2. From 1.2, use # PartialDependenceDis...
    scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_0_24_0.html
    Mon Mar 23 20:39:20 UTC 2026
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  3. Compare the effect of different scalers on data...

    cutoffs_X1 [ 1 ]], axis = 1 ) plot_distribution ( axarr [ 1 ], X [ non_outliers_mask...left , width = 0.1 , 0.22 bottom , height = 0.1 , 0.7 bottom_h...
    scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html
    Mon Mar 23 20:39:21 UTC 2026
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  4. Explicit feature map approximation for RBF kern...

    reshape ( - 1 , data . shape [ 1 ]) # title for the plots...y_max]. plt . subplot ( 1 , 3 , i + 1 ) Z = clf . predict ( flat_grid...
    scikit-learn.org/stable/auto_examples/miscellaneous/plot_kernel_approximation.html
    Mon Mar 23 20:39:20 UTC 2026
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  5. Lasso on dense and sparse data — scikit-learn 1...

    coo_matrix ( X ) alpha = 1 sparse_lasso = Lasso ( alpha =...100 ) : .3f } %" ) alpha = 0.1 sparse_lasso = Lasso ( alpha =...
    scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.html
    Mon Mar 23 20:39:22 UTC 2026
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  6. 2.7. Novelty and Outlier Detection — scikit-lea...

    array ([[ - 1 , - 1 ], [ - 2 , - 1 ], [ - 3 , - 2 ], [...Inliers are labeled 1, while outliers are labeled -1. The predict method...
    scikit-learn.org/stable/modules/outlier_detection.html
    Mon Mar 23 20:39:23 UTC 2026
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  7. Post-tuning the decision threshold for cost-sen...

    array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00,...array([1.e-06, 1.e-05, 1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00,...
    scikit-learn.org/stable/auto_examples/model_selection/plot_cost_sensitive_learning.html
    Mon Mar 23 20:39:22 UTC 2026
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  8. Lasso model selection via information criteria ...

    lasso_lars_ic [ - 1 ] . criterion_ , n_samples , lasso_lars_ic [ - 1 ] . noise_variance_...lasso_lars_ic [ - 1 ] . alphas_ == lasso_lars_ic [ - 1 ] . alpha_ )[...
    scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lars_ic.html
    Mon Mar 23 20:39:21 UTC 2026
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  9. t-SNE: The effect of various perplexity values ...

    reshape ( - 1 , 1 ), yy . ravel () . reshape ( - 1 , 1 ), ] ) color...perplexities ): ax = subplots [ 1 ][ i + 1 ] t0 = time () tsne = manifold...
    scikit-learn.org/stable/auto_examples/manifold/plot_t_sne_perplexity.html
    Mon Mar 23 20:39:22 UTC 2026
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  10. Common pitfalls in the interpretation of coeffi...

    1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04,...1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04,...
    scikit-learn.org/stable/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html
    Mon Mar 23 20:39:20 UTC 2026
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