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  1. Adjustment for chance in clustering performance...

    ylim ( bottom =- 0.05 , top = 1.05 ) plt . legend ( plots...plt . ylim ( bottom =- 0.05 , top = 1.05 ) plt . show () We observe...
    scikit-learn.org/stable/auto_examples/cluster/plot_adjusted_for_chance_measures.html
    Mon Mar 23 20:39:21 UTC 2026
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  2. Classifier comparison — scikit-learn 1.8.0 docu...

    GaussianProcessClass ( 1.0 * RBF ( 1.0 ), random_state = 42 ),...() - 0.5 , X [:, 0 ] . max () + 0.5 y_min , y_max = X [:, 1 ] ....
    scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html
    Mon Mar 23 20:39:20 UTC 2026
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  3. 3. Model selection and evaluation — scikit-lear...

    1.1. Computing cross-validated metrics 3.1.2. Cross...validation iterators 3.1.3. A note on shuffling 3.1.4. Cross validation...
    scikit-learn.org/stable/model_selection.html
    Mon Mar 23 20:39:23 UTC 2026
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  4. sg_gallery-rendered-html.css

    margin: 1.08em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered_html...margin: 1.55em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered_html...
    scikit-learn.org/stable/_static/sg_gallery-rendered-html.css
    Mon Mar 23 20:39:23 UTC 2026
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  5. sg_gallery-dataframe.css

    vertical-align: middle; padding: 0.5em 0.5em; line-height: normal; white-space:...html[data-theme="light"] { --sg-text-color: #000; --sg-tr-odd-color: #f5f5f5;...
    scikit-learn.org/stable/_static/sg_gallery-dataframe.css
    Mon Mar 23 20:39:20 UTC 2026
      526 bytes
     
  6. Search - scikit-learn 1.8.0 documentation

    Skip to main content Back to top Ctrl + K GitHub Choose version Search Ctrl + K
    scikit-learn.org/stable/search.html
    Mon Mar 23 20:39:22 UTC 2026
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  7. Release History — scikit-learn 1.8.0 documentation

    Version 1.1.2 Version 1.1.1 Version 1.1.0 Version 1.0 Version 1.0.2...Version 1.2.1 Version 1.2.0 Version 1.1 Version 1.1.3 Version...
    scikit-learn.org/stable/whats_new.html
    Mon Mar 23 20:39:21 UTC 2026
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  8. Probability Calibration for 3-class classificat...

    plot ([ 0.0 , 1.0 , 0.0 , 0.0 ], [ 0.0 , 0.0 , 1.0 , 0.0 ], "k"...be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead...
    scikit-learn.org/stable/auto_examples/calibration/plot_calibration_multiclass.html
    Mon Mar 23 20:39:20 UTC 2026
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  9. Covariance estimation — scikit-learn 1.8.0 docu...

    Examples concerning the sklearn.covariance module. Ledoit-Wolf vs OAS estimation Robust covariance estimation and Mahalanobis distances relevance Robust vs Empirical covariance estimate Shrinkage c...
    scikit-learn.org/stable/auto_examples/covariance/index.html
    Mon Mar 23 20:39:20 UTC 2026
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  10. Early stopping in Gradient Boosting — scikit-le...

    n_estimators = 1000 , max_depth = 5 , learning_rate = 0.1 , random_state...validation_fraction = 0.1 , n_iter_no_change = 10 , ) start_time =...
    scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_early_stopping.html
    Mon Mar 23 20:39:21 UTC 2026
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