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  1. load_sample_image — scikit-learn 1.6.1 document...

    Skip to main content Back to top Ctrl + K GitHub Choose version load_sample_image # sklearn.datasets. load_sample_ima...
    scikit-learn.org/stable/modules/generated/sklearn.datasets.load_sample_image.html
    Mon Apr 21 17:07:39 UTC 2025
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  2. register_parallel_backend — scikit-learn 1.6.1 ...

    Skip to main content Back to top Ctrl + K GitHub Choose version register_parallel_backend # sklearn.utils. register_p...
    scikit-learn.org/stable/modules/generated/sklearn.utils.register_parallel_backend.html
    Mon Apr 21 17:07:40 UTC 2025
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  3. sklearn.kernel_approximation — scikit-learn 1.6...

    Approximate kernel feature maps based on Fourier transforms and count sketches. User guide. See the Kernel Approximation section for further details.
    scikit-learn.org/stable/api/sklearn.kernel_approximation.html
    Mon Apr 21 17:07:39 UTC 2025
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  4. sklearn.naive_bayes — scikit-learn 1.6.1 docume...

    Naive Bayes algorithms. These are supervised learning methods based on applying Bayes’ theorem with strong (naive) feature independence assumptions. User guide. See the Naive Bayes section for furt...
    scikit-learn.org/stable/api/sklearn.naive_bayes.html
    Mon Apr 21 17:07:38 UTC 2025
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  5. sklearn.random_projection — scikit-learn 1.6.1 ...

    Random projection transformers. Random projections are a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional ...
    scikit-learn.org/stable/api/sklearn.random_projection.html
    Mon Apr 21 17:07:39 UTC 2025
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  6. Examples based on real world datasets — scikit-...

    Applications to real world problems with some medium sized datasets or interactive user interface. Compressive sensing: tomography reconstruction with L1 prior (Lasso) Faces recognition example usi...
    scikit-learn.org/stable/auto_examples/applications/index.html
    Mon Apr 21 17:07:39 UTC 2025
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  7. Gaussian Process for Machine Learning — scikit-...

    Examples concerning the sklearn.gaussian_process module. Ability of Gaussian process regression (GPR) to estimate data noise-level Comparison of kernel ridge and Gaussian process regression Forecas...
    scikit-learn.org/stable/auto_examples/gaussian_process/index.html
    Mon Apr 21 17:07:38 UTC 2025
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  8. 13. External Resources, Videos and Talks — scik...

    1. New to Scientific Python? # For...
    scikit-learn.org/stable/presentations.html
    Sat Apr 19 00:31:22 UTC 2025
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  9. Lasso, Lasso-LARS, and Elastic Net paths — scik...

    legend (( l1 [ - 1 ], l2 [ - 1 ]), ( "Lasso" , "LARS" ),...plt . legend (( l1 [ - 1 ], l2 [ - 1 ]), ( "Lasso" , "Elastic-Net"...
    scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.html
    Mon Apr 21 17:07:38 UTC 2025
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  10. Release Highlights for scikit-learn 0.22 — scik...

    pclass embarked 0 1 S 1 1 S 2 1 S 3 1 S 4 1 S Checking scikit-learn...array ([ 0 , 1 , 2 , np . nan ]) . reshape ( - 1 , 1 ) y = [ 0 ,...
    scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_0_22_0.html
    Mon Apr 21 17:07:39 UTC 2025
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