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  1. scikit-learn: machine learning in Python — scik...

    Skip to main content Back to top Ctrl + K scikit-learn Machine Learning in Python Getting Started Release Highlights ...
    scikit-learn.org/stable/index.html
    Sat Aug 23 16:32:04 UTC 2025
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  2. Comparing Random Forests and Histogram Gradient...

    In this example we compare the performance of Random Forest (RF) and Histogram Gradient Boosting (HGBT) models in terms of score and computation time for a regression dataset, though all the concep...
    scikit-learn.org/stable/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.html
    Sat Aug 23 16:32:04 UTC 2025
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  3. Post pruning decision trees with cost complexit...

    The DecisionTreeClassifier provides parameters such as min_samples_leaf and max_depth to prevent a tree from overfiting. Cost complexity pruning provides another option to control the size of a tre...
    scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html
    Sat Aug 23 16:32:04 UTC 2025
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  4. An example of K-Means++ initialization — scikit...

    An example to show the output of the sklearn.cluster.kmeans_plusplus function for generating initial seeds for clustering. K-Means++ is used as the default initialization for K-means. Total running...
    scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_plusplus.html
    Sat Aug 23 16:32:04 UTC 2025
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  5. 2.1. Gaussian mixture models — scikit-learn 1.7...

    sklearn.mixture is a package which enables one to learn Gaussian Mixture Models (diagonal, spherical, tied and full covariance matrices supported), sample them, and estimate them from data. Facilit...
    scikit-learn.org/stable/modules/mixture.html
    Sat Aug 23 16:32:03 UTC 2025
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  6. 7.1. Pipelines and composite estimators — sciki...

    To build a composite estimator, transformers are usually combined with other transformers or with predictors(such as classifiers or regressors). The most common tool used for composing estimators i...
    scikit-learn.org/stable/modules/compose.html
    Sat Aug 23 16:32:03 UTC 2025
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  7. 14. External Resources, Videos and Talks — scik...

    The scikit-learn MOOC: If you are new to scikit-learn, or looking to strengthen your understanding, we highly recommend the scikit-learn MOOC (Massive Open Online Course). The MOOC, created and mai...
    scikit-learn.org/stable/presentations.html
    Sat Aug 23 16:32:04 UTC 2025
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  8. Plot the decision surfaces of ensembles of tree...

    Plot the decision surfaces of forests of randomized trees trained on pairs of features of the iris dataset. This plot compares the decision surfaces learned by a decision tree classifier (first col...
    scikit-learn.org/stable/auto_examples/ensemble/plot_forest_iris.html
    Sat Aug 23 16:32:03 UTC 2025
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  9. homogeneity_completeness_v_measure — scikit-lea...

    Skip to main content Back to top Ctrl + K GitHub Choose version homogeneity_completeness_v_measure # sklearn.metrics....
    scikit-learn.org/stable/modules/generated/sklearn.metrics.homogeneity_completeness_v_measure.html
    Sat Aug 23 16:32:03 UTC 2025
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  10. make_sparse_spd_matrix — scikit-learn 1.7.1 doc...

    Gallery examples: Sparse inverse covariance estimation
    scikit-learn.org/stable/modules/generated/sklearn.datasets.make_sparse_spd_matrix.html
    Sat Aug 23 16:32:03 UTC 2025
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