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  1. Confusion matrix — scikit-learn 1.7.2 doc...

    Example of confusion matrix usage to evaluate the quality of the output of a classifier on the iris data set. The diagonal elements represent the number of points for which the predicted label is e...
    scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
    Fri Dec 05 17:52:55 GMT 2025
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  2. SGD: Penalties — scikit-learn 1.7.2 docum...

    Contours of where the penalty is equal to 1 for the three penalties L1, L2 and elastic-net. All of the above are supported by SGDClassifier and SGDRegressor. Total running time of the script:(0 min...
    scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_penalties.html
    Fri Dec 05 17:52:54 GMT 2025
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  3. Multioutput methods — scikit-learn 1.7.2 ...

    Examples concerning the sklearn.multioutput module. Multilabel classification using a classifier chain
    scikit-learn.org/stable/auto_examples/multioutput/index.html
    Fri Dec 05 17:52:54 GMT 2025
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  4. sklearn.ensemble — scikit-learn 1.7.2 doc...

    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
    Fri Dec 05 17:52:53 GMT 2025
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  5. sklearn.multioutput — scikit-learn 1.7.2 ...

    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
    Fri Dec 05 17:52:54 GMT 2025
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  6. sklearn.pipeline — scikit-learn 1.7.2 doc...

    Utilities to build a composite estimator as a chain of transforms and estimators. User guide. See the Pipelines and composite estimators section for further details.
    scikit-learn.org/stable/api/sklearn.pipeline.html
    Fri Dec 05 17:52:54 GMT 2025
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  7. sklearn.covariance — scikit-learn 1.7.2 d...

    Methods and algorithms to robustly estimate covariance. They estimate the covariance of features at given sets of points, as well as the precision matrix defined as the inverse of the covariance. C...
    scikit-learn.org/stable/api/sklearn.covariance.html
    Fri Dec 05 17:52:54 GMT 2025
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  8. sklearn.inspection — scikit-learn 1.7.2 d...

    Tools for model inspection. User guide. See the Inspection section for further details. Plotting:
    scikit-learn.org/stable/api/sklearn.inspection.html
    Fri Dec 05 17:52:53 GMT 2025
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  9. sklearn.manifold — scikit-learn 1.7.2 doc...

    Data embedding techniques. User guide. See the Manifold learning section for further details.
    scikit-learn.org/stable/api/sklearn.manifold.html
    Fri Dec 05 17:52:53 GMT 2025
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  10. sklearn.kernel_ridge — scikit-learn 1.7.2...

    Kernel ridge regression. User guide. See the Kernel ridge regression section for further details.
    scikit-learn.org/stable/api/sklearn.kernel_ridge.html
    Fri Dec 05 17:52:54 GMT 2025
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