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  1. Comparison of the K-Means and MiniBatchKMeans c...

    We want to compare the performance of the MiniBatchKMeans and KMeans: the MiniBatchKMeans is faster, but gives slightly different results (see Mini Batch K-Means). We will cluster a set of data, fi...
    scikit-learn.org/stable/auto_examples/cluster/plot_mini_batch_kmeans.html
    Fri Oct 10 15:14:33 UTC 2025
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  2. d2_absolute_error_score — scikit-learn 1.7.2 do...

    Skip to main content Back to top Ctrl + K GitHub Choose version d2_absolute_error_score # sklearn.metrics. d2_absolut...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.d2_absolute_error_score.html
    Fri Oct 10 15:14:33 UTC 2025
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  3. 1.11. Ensembles: Gradient boosting, random fore...

    Ensemble methods combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator. Two very famous ...
    scikit-learn.org/stable/modules/ensemble.html
    Fri Oct 10 15:14:33 UTC 2025
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  4. SVM-Anova: SVM with univariate feature selectio...

    This example shows how to perform univariate feature selection before running a SVC (support vector classifier) to improve the classification scores. We use the iris dataset (4 features) and add 36...
    scikit-learn.org/stable/auto_examples/svm/plot_svm_anova.html
    Fri Oct 10 15:14:35 UTC 2025
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  5. Plot the decision surface of decision trees tra...

    Plot the decision surface of a decision tree trained on pairs of features of the iris dataset. See decision tree for more information on the estimator. For each pair of iris features, the decision ...
    scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html
    Fri Oct 10 15:14:33 UTC 2025
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  6. Release Highlights for scikit-learn 1.3 — sciki...

    We are pleased to announce the release of scikit-learn 1.3! Many bug fixes and improvements were added, as well as some new key features. We detail below a few of the major features of this release...
    scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_3_0.html
    Fri Oct 10 15:14:33 UTC 2025
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  7. single_source_shortest_path_length — scikit-lea...

    Skip to main content Back to top Ctrl + K GitHub Choose version single_source_shortest_path_length # sklearn.utils.gr...
    scikit-learn.org/stable/modules/generated/sklearn.utils.graph.single_source_shortest_path_length....
    Thu Oct 09 16:57:49 UTC 2025
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  8. inplace_csr_row_normalize_l2 — scikit-learn 1.7...

    Skip to main content Back to top Ctrl + K GitHub Choose version inplace_csr_row_normalize_l2 # sklearn.utils.sparsefu...
    scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normaliz...
    Thu Oct 09 16:57:45 UTC 2025
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  9. Lasso, Lasso-LARS, and Elastic Net paths — scik...

    This example shows how to compute the “paths” of coefficients along the Lasso, Lasso-LARS, and Elastic Net regularization paths. In other words, it shows the relationship between the regularization...
    scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.html
    Fri Oct 10 15:14:36 UTC 2025
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  10. 5.1. Partial Dependence and Individual Conditio...

    Partial dependence plots (PDP) and individual conditional expectation (ICE) plots can be used to visualize and analyze interaction between the target response 1 and a set of input features of inter...
    scikit-learn.org/stable/modules/partial_dependence.html
    Fri Oct 10 15:14:35 UTC 2025
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