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  1. 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 Jan 19 11:28:25 GMT 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 Jan 19 11:28:23 GMT 2026
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  3. Blind source separation using FastICA — s...

    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 Jan 19 11:28:24 GMT 2026
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  4. Test with permutations the significance of a cl...

    This example demonstrates the use of permutation_test_score to evaluate the significance of a cross-validated score using permutations. Dataset: We will use the Iris plants dataset, which consists ...
    scikit-learn.org/stable/auto_examples/model_selection/plot_permutation_tests_for_classification.html
    Mon Jan 19 11:28:25 GMT 2026
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  5. Compare Stochastic learning strategies for MLPC...

    This example visualizes some training loss curves for different stochastic learning strategies, including SGD and Adam. Because of time-constraints, we use several small datasets, for which L-BFGS ...
    scikit-learn.org/stable/auto_examples/neural_networks/plot_mlp_training_curves.html
    Mon Jan 19 11:28:25 GMT 2026
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  6. Swiss Roll And Swiss-Hole Reduction — sci...

    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 Jan 19 11:28:24 GMT 2026
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  7. Multilabel classification using a classifier ch...

    This example shows how to use ClassifierChain to solve a multilabel classification problem. The most naive strategy to solve such a task is to independently train a binary classifier on each label ...
    scikit-learn.org/stable/auto_examples/multioutput/plot_classifier_chain_yeast.html
    Mon Jan 19 11:28:25 GMT 2026
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  8. Varying regularization in Multi-layer Perceptro...

    A comparison of different values for regularization parameter ‘alpha’ on synthetic datasets. The plot shows that different alphas yield different decision functions. Alpha is a parameter for regula...
    scikit-learn.org/stable/auto_examples/neural_networks/plot_mlp_alpha.html
    Mon Jan 19 11:28:25 GMT 2026
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  9. Concatenating multiple feature extraction metho...

    In many real-world examples, there are many ways to extract features from a dataset. Often it is beneficial to combine several methods to obtain good performance. This example shows how to use Feat...
    scikit-learn.org/stable/auto_examples/compose/plot_feature_union.html
    Mon Jan 19 11:28:24 GMT 2026
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  10. Effect of transforming the targets in regressio...

    In this example, we give an overview of TransformedTargetRegressor. We use two examples to illustrate the benefit of transforming the targets before learning a linear regression model. The first ex...
    scikit-learn.org/stable/auto_examples/compose/plot_transformed_target.html
    Mon Jan 19 11:28:23 GMT 2026
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