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  1. 6.1. 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
    Mon Apr 21 17:07:40 UTC 2025
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  2. 2.1. Gaussian mixture models — scikit-learn 1.6...

    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
    Mon Apr 21 17:07:39 UTC 2025
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  3. root_mean_squared_error — scikit-learn 1.6.1 do...

    Gallery examples: Features in Histogram Gradient Boosting Trees Lagged features for time series forecasting
    scikit-learn.org/stable/modules/generated/sklearn.metrics.root_mean_squared_error.html
    Mon Apr 21 17:07:39 UTC 2025
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  4. mean_squared_log_error — scikit-learn 1.6.1 doc...

    Skip to main content Back to top Ctrl + K GitHub Choose version mean_squared_log_error # sklearn.metrics. mean_square...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_log_error.html
    Mon Apr 21 17:07:39 UTC 2025
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  5. 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
    Mon Apr 21 17:07:39 UTC 2025
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  6. top_k_accuracy_score — scikit-learn 1.6.1 docum...

    Skip to main content Back to top Ctrl + K GitHub Choose version top_k_accuracy_score # sklearn.metrics. top_k_accurac...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.top_k_accuracy_score.html
    Mon Apr 21 17:07:39 UTC 2025
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  7. 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
    Mon Apr 21 17:07:39 UTC 2025
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  8. 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
    Mon Apr 21 17:07:39 UTC 2025
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  9. 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
    Mon Apr 21 17:07:38 UTC 2025
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  10. Factor Analysis (with rotation) to visualize pa...

    Investigating the Iris dataset, we see that sepal length, petal length and petal width are highly correlated. Sepal width is less redundant. Matrix decomposition techniques can uncover these latent...
    scikit-learn.org/stable/auto_examples/decomposition/plot_varimax_fa.html
    Mon Apr 21 17:07:39 UTC 2025
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