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  1. Lasso, Lasso-LARS, and Elastic Net paths — scik...

    This example shows how to compute the “paths” of coefficients along the Lasso, Lasso-LARS, and Elastic Net regularization paths. In other words, it shows the relationship between the regularization...
    scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.html
    Tue Mar 17 03:44:36 UTC 2026
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  2. 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
    Tue Mar 17 03:44:38 UTC 2026
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  3. 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
    Tue Mar 17 03:44:36 UTC 2026
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  4. SVM-Anova: SVM with univariate feature selectio...

    This example shows how to perform univariate feature selection before running a SVC (support vector classifier) to improve the classification scores. We use the iris dataset (4 features) and add 36...
    scikit-learn.org/stable/auto_examples/svm/plot_svm_anova.html
    Tue Mar 17 03:44:38 UTC 2026
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  5. Demonstration of multi-metric evaluation on cro...

    Multiple metric parameter search can be done by setting the scoring parameter to a list of metric scorer names or a dict mapping the scorer names to the scorer callables. The scores of all the scor...
    scikit-learn.org/stable/auto_examples/model_selection/plot_multi_metric_evaluation.html
    Tue Mar 17 03:44:39 UTC 2026
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  6. 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
    Tue Mar 17 03:44:39 UTC 2026
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  7. Comparison of the K-Means and MiniBatchKMeans c...

    We want to compare the performance of the MiniBatchKMeans and KMeans: the MiniBatchKMeans is faster, but gives slightly different results (see Mini Batch K-Means). We will cluster a set of data, fi...
    scikit-learn.org/stable/auto_examples/cluster/plot_mini_batch_kmeans.html
    Tue Mar 17 03:44:36 UTC 2026
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  8. 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
    Tue Mar 17 03:44:39 UTC 2026
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  9. MNIST classification using multinomial logistic...

    Here we fit a multinomial logistic regression with L1 penalty on a subset of the MNIST digits classification task. We use the SAGA algorithm for this purpose: this a solver that is fast when the nu...
    scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html
    Tue Mar 17 03:44:39 UTC 2026
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  10. Gaussian Processes regression: basic introducto...

    A simple one-dimensional regression example computed in two different ways: A noise-free case, A noisy case with known noise-level per datapoint. In both cases, the kernel’s parameters are estimate...
    scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy_targets.html
    Tue Mar 17 03:44:38 UTC 2026
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