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  1. Features in Histogram Gradient Boosting Trees —...

    Histogram-Based Gradient Boosting(HGBT) models may be one of the most useful supervised learning models in scikit-learn. They are based on a modern gradient boosting implementation comparable to Li...
    scikit-learn.org/stable/auto_examples/ensemble/plot_hgbt_regression.html
    Mon Mar 23 20:39:22 UTC 2026
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  2. Recursive feature elimination with cross-valida...

    A Recursive Feature Elimination (RFE) example with automatic tuning of the number of features selected with cross-validation. Data generation: We build a classification task using 3 informative fea...
    scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_with_cross_validation.html
    Mon Mar 23 20:39:20 UTC 2026
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  3. Blind source separation using FastICA — scikit-...

    An example of estimating sources from noisy data. Independent component analysis (ICA) is used to estimate sources given noisy measurements. Imagine 3 instruments playing simultaneously and 3 micro...
    scikit-learn.org/stable/auto_examples/decomposition/plot_ica_blind_source_separation.html
    Mon Mar 23 20:39:22 UTC 2026
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  4. Early stopping of Stochastic Gradient Descent —...

    Stochastic Gradient Descent is an optimization technique which minimizes a loss function in a stochastic fashion, performing a gradient descent step sample by sample. In particular, it is a very ef...
    scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_early_stopping.html
    Mon Mar 23 20:39:21 UTC 2026
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  5. Swiss Roll And Swiss-Hole Reduction — scikit-le...

    This notebook seeks to compare two popular non-linear dimensionality techniques, T-distributed Stochastic Neighbor Embedding (t-SNE) and Locally Linear Embedding (LLE), on the classic Swiss Roll da...
    scikit-learn.org/stable/auto_examples/manifold/plot_swissroll.html
    Mon Mar 23 20:39:20 UTC 2026
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  6. Plot Ridge coefficients as a function of the re...

    Shows the effect of collinearity in the coefficients of an estimator. Ridge Regression is the estimator used in this example. Each color represents a different feature of the coefficient vector, an...
    scikit-learn.org/stable/auto_examples/linear_model/plot_ridge_path.html
    Mon Mar 23 20:39:22 UTC 2026
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  7. Image denoising using kernel PCA — scikit-learn...

    This example shows how to use KernelPCA to denoise images. In short, we take advantage of the approximation function learned during fit to reconstruct the original image. We will compare the result...
    scikit-learn.org/stable/auto_examples/applications/plot_digits_denoising.html
    Mon Mar 23 20:39:21 UTC 2026
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  8. Outlier detection on a real data set — scikit-l...

    This example illustrates the need for robust covariance estimation on a real data set. It is useful both for outlier detection and for a better understanding of the data structure. We selected two ...
    scikit-learn.org/stable/auto_examples/applications/plot_outlier_detection_wine.html
    Mon Mar 23 20:39:21 UTC 2026
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  9. Poisson regression and non-normal loss — scikit...

    This example illustrates the use of log-linear Poisson regression on the French Motor Third-Party Liability Claims dataset from 1 and compares it with a linear model fitted with the usual least squ...
    scikit-learn.org/stable/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.html
    Mon Mar 23 20:39:20 UTC 2026
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  10. Permutation Importance with Multicollinear or C...

    In this example, we compute the permutation_importance of the features to a trained RandomForestClassifier using the Breast cancer Wisconsin (diagnostic) dataset. The model can easily get about 97%...
    scikit-learn.org/stable/auto_examples/inspection/plot_permutation_importance_multicollinear.html
    Mon Mar 23 20:39:20 UTC 2026
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