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  1. make_sparse_coded_signal — scikit-learn 1.5.2 d...

    Gallery examples: Orthogonal Matching Pursuit
    scikit-learn.org/stable/modules/generated/sklearn.datasets.make_sparse_coded_signal.html
    Sat Nov 23 04:49:16 UTC 2024
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  2. reconstruct_from_patches_2d — scikit-learn 1.5....

    Gallery examples: Image denoising using dictionary learning
    scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.image.reconstruct_from_patch...
    Sat Nov 23 04:49:15 UTC 2024
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  3. normalized_mutual_info_score — scikit-learn 1.5...

    Gallery examples: Adjustment for chance in clustering performance evaluation
    scikit-learn.org/stable/modules/generated/sklearn.metrics.normalized_mutual_info_score.html
    Sat Nov 23 04:49:14 UTC 2024
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  4. precision_recall_fscore_support — scikit-learn ...

    Skip to main content Back to top Ctrl + K GitHub precision_recall_fscore_support # sklearn.metrics. precision_recall_...
    scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html
    Sat Nov 23 04:49:16 UTC 2024
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  5. incr_mean_variance_axis — scikit-learn 1.5.2 do...

    Skip to main content Back to top Ctrl + K GitHub incr_mean_variance_axis # sklearn.utils.sparsefuncs. incr_mean_varia...
    scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs.incr_mean_variance_axis.html
    Sat Nov 23 04:49:14 UTC 2024
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  6. 13. External Resources, Videos and Talks — scik...

    New to Scientific Python?: For those that are still new to the scientific Python ecosystem, we highly recommend the Python Scientific Lecture Notes. This will help you find your footing a bit and w...
    scikit-learn.org/stable/presentations.html
    Fri Nov 22 23:53:26 UTC 2024
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  7. 2.1. Gaussian mixture models — scikit-learn 1.5...

    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
    Fri Nov 22 23:53:27 UTC 2024
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  8. Comparison of LDA and PCA 2D projection of Iris...

    The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 attributes: sepal length, sepal width, petal length and petal width. Principal Component Analysis (PCA)...
    scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html
    Sat Nov 23 04:49:14 UTC 2024
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  9. 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 Nov 23 04:49:16 UTC 2024
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  10. 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
    Sat Nov 23 04:49:16 UTC 2024
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