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  1. Explicit feature map approximation for RBF kern...

    kernel_svm = svm . SVC ( gamma = 0.2 ) linear_svm = svm . LinearSVC...feature_map_fourier = RBFSampler ( gamma = 0.2 , random_state = 1 ) feature_map_nystroem...
    scikit-learn.org/stable/auto_examples/miscellaneous/plot_kernel_approximation.html
    Thu Apr 10 21:02:03 UTC 2025
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  2. Effect of transforming the targets in regressio...

    y = make_regression ( n_samples = 10_000 , noise = 100 ,...ax1 ) = plt . subplots ( 1 , 2 , sharey = True ) ridge_cv = RidgeCV...
    scikit-learn.org/stable/auto_examples/compose/plot_transformed_target.html
    Thu Apr 10 21:02:03 UTC 2025
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  3. RocCurveDisplay — scikit-learn 1.6.1 documentation

    pos_label = None , name = None , ax = None , plot_chance_level = False...roc_auc = None , estimator_name = None , pos_label = None ) [source]...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.RocCurveDisplay.html
    Thu Apr 10 21:02:05 UTC 2025
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  4. Novelty detection with Local Outlier Factor (LO...

    X_outliers = np . random . uniform ( low =- 4 , high = 4 , size = ( 20...1 ], c = "blueviolet" , s = s , edgecolors = "k" ) c = plt . scatter...
    scikit-learn.org/stable/auto_examples/neighbors/plot_lof_novelty_detection.html
    Thu Apr 10 21:02:04 UTC 2025
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  5. Column Transformer with Mixed Types — scikit-le...

    y = fetch_openml ( "titanic" , version = 1 , as_frame = True...attribute: # X = titanic.frame.drop('survived', axis=1) # y = titanic.frame['survived']...
    scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html
    Thu Apr 10 21:02:03 UTC 2025
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  6. Probability calibration of classifiers — scikit...

    ): this_X = X_train [ y_train == this_y ] this_sw = sw_train [...n_samples = n_samples , centers = centers , shuffle = False , random_state...
    scikit-learn.org/stable/auto_examples/calibration/plot_calibration.html
    Thu Apr 10 21:02:03 UTC 2025
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  7. Comparing random forests and the multi-output m...

    edgecolor = "k" , c = "navy" , s = s , marker = "s" , alpha = a , label...edgecolor = "k" , c = "c" , s = s , marker = "^" , alpha = a , label...
    scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html
    Thu Apr 10 21:02:03 UTC 2025
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  8. DetCurveDisplay — scikit-learn 1.6.1 documentation

    sample_weight = None , pos_label = None , name = None , ax = None ,..., test_size = 0.4 , random_state = 0 ) >>> clf = SVC ( random_state...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.DetCurveDisplay.html
    Thu Apr 10 21:02:03 UTC 2025
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  9. Forecasting of CO2 level on Mona Loa dataset us...

    fetch_openml co2 = fetch_openml ( data_id = 41187 , as_frame = True ) co2...length_scale = 1.0 , periodicity = 1.0 , periodicity_bounds = "fixed"...
    scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_co2.html
    Thu Apr 10 21:02:03 UTC 2025
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  10. Demonstration of k-means assumptions — scikit-l...

    ( X [ y == 0 ][: 500 ], X [ y == 1 ][: 100 ], X [ y == 2 ][: 10...axs = plt . subplots ( nrows = 2 , ncols = 2 , figsize = ( 12...
    scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_assumptions.html
    Thu Apr 10 21:02:04 UTC 2025
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