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  1. Release Highlights for scikit-learn 1.5 — sciki...

    We are pleased to announce the release of scikit-learn 1.5! Many bug fixes and improvements were added, as well as some key new features. Below we detail the highlights of this release. For an exha...
    scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_5_0.html
    Tue Mar 17 03:44:36 UTC 2026
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  2. Comparing different clustering algorithms on to...

    This example shows characteristics of different clustering algorithms on datasets that are “interesting” but still in 2D. With the exception of the last dataset, the parameters of each of these dat...
    scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html
    Tue Mar 17 03:44:39 UTC 2026
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  3. Various Agglomerative Clustering on a 2D embedd...

    An illustration of various linkage option for agglomerative clustering on a 2D embedding of the digits dataset. The goal of this example is to show intuitively how the metrics behave, and not to fi...
    scikit-learn.org/stable/auto_examples/cluster/plot_digits_linkage.html
    Tue Mar 17 03:44:38 UTC 2026
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  4. Plot classification boundaries with different S...

    This example shows how different kernels in a SVC(Support Vector Classifier) influence the classification boundaries in a binary, two-dimensional classification problem. SVCs aim to find a hyperpla...
    scikit-learn.org/stable/auto_examples/svm/plot_svm_kernels.html
    Tue Mar 17 03:44:38 UTC 2026
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  5. Release Highlights for scikit-learn 1.6 — sciki...

    We are pleased to announce the release of scikit-learn 1.6! Many bug fixes and improvements were added, as well as some key new features. Below we detail the highlights of this release. For an exha...
    scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_6_0.html
    Tue Mar 17 03:44:39 UTC 2026
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  6. Comparison of kernel ridge and Gaussian process...

    This example illustrates differences between a kernel ridge regression and a Gaussian process regression. Both kernel ridge regression and Gaussian process regression are using a so-called “kernel ...
    scikit-learn.org/stable/auto_examples/gaussian_process/plot_compare_gpr_krr.html
    Tue Mar 17 03:44:39 UTC 2026
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  7. 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
    Tue Mar 17 03:44:36 UTC 2026
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  8. Receiver Operating Characteristic (ROC) with cr...

    This example presents how to estimate and visualize the variance of the Receiver Operating Characteristic (ROC) metric using cross-validation. ROC curves typically feature true positive rate (TPR) ...
    scikit-learn.org/stable/auto_examples/model_selection/plot_roc_crossval.html
    Tue Mar 17 03:44:39 UTC 2026
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  9. 7.9. Transforming the prediction target (y) — s...

    Transforming the prediction target ( y): These are transformers that are not intended to be used on features, only on supervised learning targets. See also Transforming target in regression if you ...
    scikit-learn.org/stable/modules/preprocessing_targets.html
    Tue Mar 17 03:44:39 UTC 2026
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  10. Fitting an Elastic Net with a precomputed Gram ...

    see the documentation for the sample_weight parameter...nbviewer.org. ElasticNet ? Documentation for ElasticNet i Fitted...
    scikit-learn.org/stable/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_...
    Tue Mar 17 03:44:36 UTC 2026
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