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  1. sklearn.multioutput — scikit-learn 1.7.1 docume...

    Multioutput regression and classification. The estimators provided in this module are meta-estimators: they require a base estimator to be provided in their constructor. The meta-estimator extends ...
    scikit-learn.org/stable/api/sklearn.multioutput.html
    Wed Sep 03 15:29:58 UTC 2025
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  2. sklearn.inspection — scikit-learn 1.7.1 documen...

    Tools for model inspection. User guide. See the Inspection section for further details. Plotting:
    scikit-learn.org/stable/api/sklearn.inspection.html
    Wed Sep 03 15:29:58 UTC 2025
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  3. sklearn.ensemble — scikit-learn 1.7.1 documenta...

    Ensemble-based methods for classification, regression and anomaly detection. User guide. See the Ensembles: Gradient boosting, random forests, bagging, voting, stacking section for further details.
    scikit-learn.org/stable/api/sklearn.ensemble.html
    Wed Sep 03 15:30:00 UTC 2025
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  4. load_linnerud — scikit-learn 1.7.1 documentation

    Skip to main content Back to top Ctrl + K GitHub Choose version load_linnerud # sklearn.datasets. load_linnerud ( * ,...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.load_linnerud.html
    Wed Sep 03 15:29:59 UTC 2025
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  5. Plot different SVM classifiers in the iris data...

    plot the support vectors C = 1.0 # SVM regularization parameter...) X0 , X1 = X [:, 0 ], X [:, 1 ] for clf , title , ax in zip...
    scikit-learn.org/stable/auto_examples/svm/plot_iris_svc.html
    Wed Sep 03 15:30:01 UTC 2025
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  6. A demo of structured Ward hierarchical clusteri...

    ( - 1 , 1 )) Define structure of the data...
    scikit-learn.org/stable/auto_examples/cluster/plot_coin_ward_segmentation.html
    Wed Sep 03 15:29:59 UTC 2025
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  7. 7.9. Transforming the prediction target (y) — s...

    1, 1, 1], [0, 0, 1, 0, 0], [1, 1, 0, 1, 0], [1, 1, 1, 1,...1, 1], [1, 1, 1, 0, 0]]) For more information about multilabel...
    scikit-learn.org/stable/modules/preprocessing_targets.html
    Wed Sep 03 15:29:59 UTC 2025
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  8. 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
    Wed Sep 03 15:30:01 UTC 2025
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  9. Fitting an Elastic Net with a precomputed Gram ...

    -1.67451144e+02], [-4.48938813e+02, 1.00768662e+05, 1.19112072e+02,......, -1.07963978e+03, 7.47987268e+01, -5.76195467e+02], [-1.03237920e+03,...
    scikit-learn.org/stable/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_...
    Wed Sep 03 15:29:59 UTC 2025
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  10. Compressive sensing: tomography reconstruction ...

    floor_x + 1 )), np . hstack (( 1 - alpha , alpha ))...int ), ( points [ 1 ]) . astype ( int )] = 1 mask = ndimage ....
    scikit-learn.org/stable/auto_examples/applications/plot_tomography_l1_reconstruction.html
    Wed Sep 03 15:29:59 UTC 2025
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