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  1. Post-hoc tuning the cut-off point of decision f...

    steps [('standardscaler', ...), ('logisticregression', ...)] transform_input...Pipeline(steps=[('standardscaler', StandardScaler()), ('logisticregression',...
    scikit-learn.org/stable/auto_examples/model_selection/plot_tuned_decision_threshold.html
    Mon Sep 22 13:26:35 UTC 2025
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  2. Confusion matrix scikit-learn 1.7.2 documenta...

    confusion matrix [[1. 0. 0. ] [0. 0.62 0.38] [0. 0. 1. ]] # Authors: The...classifier = svm . SVC ( kernel = "linear" , C = 0.01 ) . fit ( X_train...
    scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
    Mon Sep 22 13:26:35 UTC 2025
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  3. Robust linear estimator fitting scikit-learn ...

    "-" , "Theil-Sen" : "-." , "RANSAC" : "--" , "HuberRegressor"...= ( 5 , 4 )) plt . plot ( this_X [:, 0 ], this_y , "b+" ) for...
    scikit-learn.org/stable/auto_examples/linear_model/plot_robust_fit.html
    Mon Sep 22 13:26:33 UTC 2025
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  4. Detection error tradeoff (DET) curve scikit-l...

    classifiers . items (): ( color , linestyle ) = ( ( "black" , "--" ) if...make_pipeline ( StandardScaler (), LinearSVC ( C = 0.025 )), "Random...
    scikit-learn.org/stable/auto_examples/model_selection/plot_det.html
    Mon Sep 22 13:26:33 UTC 2025
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  5. Comparing Nearest Neighbors with and without Ne...

    "#FF0000" , "#00FF00" , "#0000FF" ]) names = [ "KNN" , "NCA,...{} )" . format ( name , n_neighbors )) plt . text ( 0.9 , 0.1 ,...
    scikit-learn.org/stable/auto_examples/neighbors/plot_nca_classification.html
    Mon Sep 22 13:26:35 UTC 2025
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  6. Recursive feature elimination scikit-learn 1....

    "scaler" , MinMaxScaler ()), ( "rfe" , RFE ( estimator = LogisticRegression...LogisticRegression (), n_features_to_select = 1 , step = 1 )), ] ) pipe . fit...
    scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html
    Mon Sep 22 13:26:34 UTC 2025
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  7. Model-based and sequential feature selection ...

    Statistics (with discussion), 407-499. (https://web.stanford.edu/~ha...= np . logspace ( - 6 , 6 , num = 5 )) . fit ( X , y ) importance...
    scikit-learn.org/stable/auto_examples/feature_selection/plot_select_from_model_diabetes.html
    Mon Sep 22 13:26:33 UTC 2025
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  8. Permutation Importance vs Random Forest Feature...

    'random_cat']), ('num', SimpleImputer(), ['age', 'sibsp', 'parch', 'fare',...', unknown_value=-1), ['pclass', 'sex', 'embarked', 'random_cat']),...
    scikit-learn.org/stable/auto_examples/inspection/plot_permutation_importance.html
    Mon Sep 22 13:26:34 UTC 2025
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  9. Ledoit-Wolf vs OAS estimation scikit-learn 1....

    oa_mse . mean ( 1 ), yerr = oa_mse . std ( 1 ), label = "OAS" , color...( 1 ), label = "Ledoit-Wolf" , color = "navy" , lw = 2 , ) plt...
    scikit-learn.org/stable/auto_examples/covariance/plot_lw_vs_oas.html
    Mon Sep 22 13:26:35 UTC 2025
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  10. Plot randomly generated multilabel dataset sc...

    Class P(C) P(w0|C) P(w1|C) red 0.32 0.55 0.45 blue 0.26 0.79 0.21...= np . array ( [ "!" , "#FF3333" , # red "#0198E1" , # blue "#BF5FFF"...
    scikit-learn.org/stable/auto_examples/datasets/plot_random_multilabel_dataset.html
    Mon Sep 22 13:26:33 UTC 2025
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