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  1. Demonstration of k-means assumptions — scikit-l...

    the example Clustering text documents using k-means ). In the case...
    scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_assumptions.html
    Thu Jul 03 11:42:05 UTC 2025
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  2. Version 0.24 — scikit-learn 1.7.0 documentation

    previously didn’t work as documented – or according to reasonable...by Nathan C. . Code and documentation contributors Thanks to everyone...
    scikit-learn.org/stable/whats_new/v0.24.html
    Thu Jul 03 11:42:05 UTC 2025
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  3. Various Agglomerative Clustering on a 2D embedd...

    An illustration of various linkage option for agglomerative clustering on a 2D embedding of the digits dataset. The goal of this example is to show intuitively how the metrics behave, and not to fi...
    scikit-learn.org/stable/auto_examples/cluster/plot_digits_linkage.html
    Thu Jul 03 11:42:05 UTC 2025
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  4. Comparison of F-test and mutual information — s...

    This example illustrates the differences between univariate F-test statistics and mutual information. We consider 3 features x_1, x_2, x_3 distributed uniformly over [0, 1], the target depends on t...
    scikit-learn.org/stable/auto_examples/feature_selection/plot_f_test_vs_mi.html
    Thu Jul 03 11:42:05 UTC 2025
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  5. 7.7. Kernel Approximation — scikit-learn 1.7.0 ...

    This submodule contains functions that approximate the feature mappings that correspond to certain kernels, as they are used for example in support vector machines (see Support Vector Machines). Th...
    scikit-learn.org/stable/modules/kernel_approximation.html
    Thu Jul 03 11:42:06 UTC 2025
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  6. Comparing different clustering algorithms on to...

    This example shows characteristics of different clustering algorithms on datasets that are “interesting” but still in 2D. With the exception of the last dataset, the parameters of each of these dat...
    scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html
    Thu Jul 03 11:42:05 UTC 2025
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  7. Face completion with a multi-output estimators ...

    This example shows the use of multi-output estimator to complete images. The goal is to predict the lower half of a face given its upper half. The first column of images shows true faces. The next ...
    scikit-learn.org/stable/auto_examples/miscellaneous/plot_multioutput_face_completion.html
    Thu Jul 03 11:42:06 UTC 2025
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  8. 1.8. Cross decomposition — scikit-learn 1.7.0 d...

    The cross decomposition module contains supervised estimators for dimensionality reduction and regression, belonging to the “Partial Least Squares” family. Cross decomposition algorithms find the f...
    scikit-learn.org/stable/modules/cross_decomposition.html
    Thu Jul 03 11:42:05 UTC 2025
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  9. SVM: Separating hyperplane for unbalanced class...

    Find the optimal separating hyperplane using an SVC for classes that are unbalanced. We first find the separating plane with a plain SVC and then plot (dashed) the separating hyperplane with automa...
    scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane_unbalanced.html
    Thu Jul 03 11:42:05 UTC 2025
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  10. SGD: Maximum margin separating hyperplane — sci...

    Plot the maximum margin separating hyperplane within a two-class separable dataset using a linear Support Vector Machines classifier trained using SGD. Total running time of the script:(0 minutes 0...
    scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_separating_hyperplane.html
    Thu Jul 03 11:42:06 UTC 2025
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