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  1. Manage users and access to App Search | App Sea...

    Search IMPORTANT : This documentation is no longer updated. Refer...version policy and the latest documentation . Manage users and access...
    www.elastic.co/guide/en/app-search/current/security-and-users.html
    Tue Jul 29 14:26:41 UTC 2025
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  2. Multiclass sparse logistic regression on 20newg...

    logistic regression to classify documents from the newgroups20 dataset....
    scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.html
    Fri Oct 10 15:14:33 UTC 2025
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  3. Release Highlights for scikit-learn 1.4 — sciki...

    Documentation for RandomForestClassifi...None One can access the documentation of the estimator by clicking...
    scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_4_0.html
    Fri Oct 10 15:14:36 UTC 2025
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  4. Joint feature selection with multi-task Lasso —...

    The multi-task lasso allows to fit multiple regression problems jointly enforcing the selected features to be the same across tasks. This example simulates sequential measurements, each task is a t...
    scikit-learn.org/stable/auto_examples/linear_model/plot_multi_task_lasso_support.html
    Fri Oct 10 15:14:33 UTC 2025
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  5. Illustration of prior and posterior Gaussian pr...

    This example illustrates the prior and posterior of a GaussianProcessRegressor with different kernels. Mean, standard deviation, and 5 samples are shown for both prior and posterior distributions. ...
    scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_prior_posterior.html
    Fri Oct 10 15:14:35 UTC 2025
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  6. Gradient Boosting Out-of-Bag estimates — scikit...

    Out-of-bag (OOB) estimates can be a useful heuristic to estimate the “optimal” number of boosting iterations. OOB estimates are almost identical to cross-validation estimates but they can be comput...
    scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_oob.html
    Fri Oct 10 15:14:35 UTC 2025
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  7. 1.4. Support Vector Machines — scikit-learn 1.7...

    Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection. The advantages of support vector machines are: Effective in high ...
    scikit-learn.org/stable/modules/svm.html
    Fri Oct 10 15:14:35 UTC 2025
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  8. Multiclass Receiver Operating Characteristic (R...

    This example describes the use of the Receiver Operating Characteristic (ROC) metric to evaluate the quality of multiclass classifiers. ROC curves typically feature true positive rate (TPR) on the ...
    scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
    Fri Oct 10 15:14:35 UTC 2025
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  9. Target Encoder’s Internal Cross fitting — sciki...

    The TargetEncoder replaces each category of a categorical feature with the shrunk mean of the target variable for that category. This method is useful in cases where there is a strong relationship ...
    scikit-learn.org/stable/auto_examples/preprocessing/plot_target_encoder_cross_val.html
    Fri Oct 10 15:14:36 UTC 2025
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  10. mean_absolute_percentage_error — scikit-learn 1...

    Gallery examples: Lagged features for time series forecasting
    scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html
    Fri Oct 10 15:14:35 UTC 2025
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