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  1. 2.2. Manifold learning — scikit-learn 1.6.1 doc...

    Look for the bare necessities, The simple bare necessities, Forget about your worries and your strife, I mean the bare necessities, Old Mother Nature’s recipes, That bring the bare necessities of l...
    scikit-learn.org/stable/modules/manifold.html
    Mon Apr 21 17:07:39 UTC 2025
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  2. Linear and Quadratic Discriminant Analysis with...

    This example plots the covariance ellipsoids of each class and the decision boundary learned by LinearDiscriminantAnalysis(LDA) and QuadraticDiscriminantAnalysis(QDA). The ellipsoids display the do...
    scikit-learn.org/stable/auto_examples/classification/plot_lda_qda.html
    Mon Apr 21 17:07:39 UTC 2025
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  3. Robust covariance estimation and Mahalanobis di...

    This example shows covariance estimation with Mahalanobis distances on Gaussian distributed data. For Gaussian distributed data, the distance of an observation x_i to the mode of the distribution c...
    scikit-learn.org/stable/auto_examples/covariance/plot_mahalanobis_distances.html
    Mon Apr 21 17:07:38 UTC 2025
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  4. Multi-class AdaBoosted Decision Trees — scikit-...

    This example shows how boosting can improve the prediction accuracy on a multi-label classification problem. It reproduces a similar experiment as depicted by Figure 1 in Zhu et al 1. The core prin...
    scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_multiclass.html
    Mon Apr 21 17:07:38 UTC 2025
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  5. Comparison between grid search and successive h...

    This example compares the parameter search performed by HalvingGridSearchCV and GridSearchCV. We first define the parameter space for an SVC estimator, and compute the time required to train a Halv...
    scikit-learn.org/stable/auto_examples/model_selection/plot_successive_halving_heatmap.html
    Mon Apr 21 17:07:39 UTC 2025
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  6. Overview of multiclass training meta-estimators...

    In this example, we discuss the problem of classification when the target variable is composed of more than two classes. This is called multiclass classification. In scikit-learn, all estimators su...
    scikit-learn.org/stable/auto_examples/multiclass/plot_multiclass_overview.html
    Mon Apr 21 17:07:38 UTC 2025
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  7. Robust linear model estimation using RANSAC — s...

    In this example, we see how to robustly fit a linear model to faulty data using the RANSAC algorithm. The ordinary linear regressor is sensitive to outliers, and the fitted line can easily be skewe...
    scikit-learn.org/stable/auto_examples/linear_model/plot_ransac.html
    Mon Apr 21 17:07:38 UTC 2025
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  8. HuberRegressor vs Ridge on dataset with strong ...

    Fit Ridge and HuberRegressor on a dataset with outliers. The example shows that the predictions in ridge are strongly influenced by the outliers present in the dataset. The Huber regressor is less ...
    scikit-learn.org/stable/auto_examples/linear_model/plot_huber_vs_ridge.html
    Mon Apr 21 17:07:38 UTC 2025
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  9. Class Likelihood Ratios to measure classificati...

    This example demonstrates the class_likelihood_ratios function, which computes the positive and negative likelihood ratios ( LR+, LR-) to assess the predictive power of a binary classifier. As we w...
    scikit-learn.org/stable/auto_examples/model_selection/plot_likelihood_ratios.html
    Mon Apr 21 17:07:38 UTC 2025
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  10. Compare the effect of different scalers on data...

    Feature 0 (median income in a block) and feature 5 (average house occupancy) of the California Housing dataset have very different scales and contain some very large outliers. These two characteris...
    scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html
    Mon Apr 21 17:07:39 UTC 2025
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