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  1. Factor Analysis (with rotation) to visualize pa...

    Investigating the Iris dataset, we see that sepal length, petal length and petal width are highly correlated. Sepal width is less redundant. Matrix decomposition techniques can uncover these latent...
    scikit-learn.org/stable/auto_examples/decomposition/plot_varimax_fa.html
    Mon Mar 23 20:39:21 UTC 2026
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  2. Comparison of kernel ridge regression and SVR —...

    Both kernel ridge regression (KRR) and SVR learn a non-linear function by employing the kernel trick, i.e., they learn a linear function in the space induced by the respective kernel which correspo...
    scikit-learn.org/stable/auto_examples/miscellaneous/plot_kernel_ridge_regression.html
    Mon Mar 23 20:39:20 UTC 2026
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  3. Manifold Learning methods on a severed sphere —...

    An application of the different Manifold learning techniques on a spherical data-set. Here one can see the use of dimensionality reduction in order to gain some intuition regarding the manifold lea...
    scikit-learn.org/stable/auto_examples/manifold/plot_manifold_sphere.html
    Mon Mar 23 20:39:21 UTC 2026
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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
    Mon Mar 23 20:39:20 UTC 2026
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  5. 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
    Mon Mar 23 20:39:21 UTC 2026
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  6. 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
    Mon Mar 23 20:39:22 UTC 2026
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  7. 7.1. Pipelines and composite estimators — sciki...

    To build a composite estimator, transformers are usually combined with other transformers or with predictors(such as classifiers or regressors). The most common tool used for composing estimators i...
    scikit-learn.org/stable/modules/compose.html
    Mon Mar 23 20:39:20 UTC 2026
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  8. 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
    Mon Mar 23 20:39:21 UTC 2026
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  9. scikit-learn: machine learning in Python — scik...

    Skip to main content Back to top Ctrl + K scikit-learn Machine Learning in Python Getting Started Release Highlights ...
    scikit-learn.org/stable/index.html
    Mon Mar 23 20:39:21 UTC 2026
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  10. Demo of affinity propagation clustering algorit...

    Reference: Brendan J. Frey and Delbert Dueck, “Clustering by Passing Messages Between Data Points”, Science Feb. 2007 Generate sample data: Compute Affinity Propagation: Plot result: Total running ...
    scikit-learn.org/stable/auto_examples/cluster/plot_affinity_propagation.html
    Mon Mar 23 20:39:22 UTC 2026
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