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  1. 7.6. Random Projection — scikit-learn 1.7.0 doc...

    The sklearn.random_projection module implements a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional varianc...
    scikit-learn.org/stable/modules/random_projection.html
    Tue Jul 08 15:58:48 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
    Tue Jul 08 15:58:47 UTC 2025
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  3. 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
    Tue Jul 08 15:58:49 UTC 2025
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  4. 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
    Tue Jul 08 15:58:49 UTC 2025
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  5. 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 Jul 08 15:58:49 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
    Tue Jul 08 15:58:49 UTC 2025
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  7. SVM: Maximum margin separating hyperplane — sci...

    Plot the maximum margin separating hyperplane within a two-class separable dataset using a Support Vector Machine classifier with linear kernel. Total running time of the script:(0 minutes 0.065 se...
    scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane.html
    Tue Jul 08 15:58:50 UTC 2025
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  8. 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 Jul 08 15:58:47 UTC 2025
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  9. Label Propagation digits: Demonstrating perform...

    This example demonstrates the power of semisupervised learning by training a Label Spreading model to classify handwritten digits with sets of very few labels. The handwritten digit dataset has 179...
    scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_digits.html
    Tue Jul 08 15:58:49 UTC 2025
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  10. 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 Jul 08 15:58:47 UTC 2025
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