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  1. Overview of multiclass training meta-estimators...

    tree classifier is working quite well. One-vs-one and the error-correcting...
    scikit-learn.org/stable/auto_examples/multiclass/plot_multiclass_overview.html
    Thu May 02 15:42:35 UTC 2024
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  2. Comparison of Calibration of Classifiers — scik...

    Note that this split is quite unusual: the goal is to obtain...
    scikit-learn.org/stable/auto_examples/calibration/plot_compare_calibration.html
    Thu May 02 15:42:33 UTC 2024
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  3. Model-based and sequential feature selection — ...

    It is quite remarkable considering that...
    scikit-learn.org/stable/auto_examples/feature_selection/plot_select_from_model_diabetes.html
    Thu May 02 15:42:32 UTC 2024
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  4. Getting Started — scikit-learn 1.4.2 documentation

    Quite often, it is not clear what...
    scikit-learn.org/stable/getting_started.html
    Thu May 02 15:42:32 UTC 2024
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  5. 1.9. Naive Bayes — scikit-learn 1.4.2 documenta...

    Bayes classifiers have worked quite well in many real-world situations,...
    scikit-learn.org/stable/modules/naive_bayes.html
    Thu May 02 15:42:32 UTC 2024
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  6. 2.2. Manifold learning — scikit-learn 1.4.2 doc...

    The optimization is quite difficult and the computation...
    scikit-learn.org/stable/modules/manifold.html
    Thu May 02 15:42:34 UTC 2024
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  7. feature_extraction.rst.txt

    however the default values are quite reasonable (please see the :ref:`reference...of words representation is quite simplistic but surprisingly...
    scikit-learn.org/stable/_sources/modules/feature_extraction.rst.txt
    Thu May 02 15:42:34 UTC 2024
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