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  1. 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 Dec 29 13:14:48 GMT 2025
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  2. 11. Common pitfalls and recommended practices &...

    The purpose of this chapter is to illustrate some common pitfalls and anti-patterns that occur when using scikit-learn. It provides examples of what not to do, along with a corresponding correct ex...
    scikit-learn.org/stable/common_pitfalls.html
    Mon Dec 29 13:14:48 GMT 2025
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  3. 14. External Resources, Videos and Talks &#8212...

    The scikit-learn MOOC: If you are new to scikit-learn, or looking to strengthen your understanding, we highly recommend the scikit-learn MOOC (Massive Open Online Course). The MOOC, created and mai...
    scikit-learn.org/stable/presentations.html
    Mon Dec 29 13:14:49 GMT 2025
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  4. 2.1. Gaussian mixture models — scikit-lea...

    sklearn.mixture is a package which enables one to learn Gaussian Mixture Models (diagonal, spherical, tied and full covariance matrices supported), sample them, and estimate them from data. Facilit...
    scikit-learn.org/stable/modules/mixture.html
    Mon Dec 29 13:14:48 GMT 2025
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  5. Gaussian Mixture Model Ellipsoids — sciki...

    Plot the confidence ellipsoids of a mixture of two Gaussians obtained with Expectation Maximisation ( GaussianMixture class) and Variational Inference ( BayesianGaussianMixture class models with a ...
    scikit-learn.org/stable/auto_examples/mixture/plot_gmm.html
    Mon Dec 29 13:14:49 GMT 2025
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  6. Compare cross decomposition methods — sci...

    Simple usage of various cross decomposition algorithms: PLSCanonical, PLSRegression, with multivariate response, a.k.a. PLS2, PLSRegression, with univariate response, a.k.a. PLS1, CCA. Given 2 mult...
    scikit-learn.org/stable/auto_examples/cross_decomposition/plot_compare_cross_decomposition.html
    Mon Dec 29 13:14:49 GMT 2025
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  7. Hierarchical clustering with and without struct...

    This example demonstrates hierarchical clustering with and without connectivity constraints. It shows the effect of imposing a connectivity graph to capture local structure in the data. Without con...
    scikit-learn.org/stable/auto_examples/cluster/plot_ward_structured_vs_unstructured.html
    Mon Dec 29 13:14:48 GMT 2025
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  8. Decision Tree Regression with AdaBoost — ...

    A decision tree is boosted using the AdaBoost.R2 1 algorithm on a 1D sinusoidal dataset with a small amount of Gaussian noise. 299 boosts (300 decision trees) is compared with a single decision tre...
    scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_regression.html
    Mon Dec 29 13:14:48 GMT 2025
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  9. 2.2. Manifold learning — scikit-learn 1.8...

    Look for the bare necessities, The simple bare necessities, Forget about your worries and your strife, I mean the bare necessities, Old Mother Nature’s recipes, That bring the bare necessities of l...
    scikit-learn.org/stable/modules/manifold.html
    Mon Dec 29 13:14:48 GMT 2025
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  10. Comparing Nearest Neighbors with and without Ne...

    An example comparing nearest neighbors classification with and without Neighborhood Components Analysis. It will plot the class decision boundaries given by a Nearest Neighbors classifier when usin...
    scikit-learn.org/stable/auto_examples/neighbors/plot_nca_classification.html
    Mon Dec 29 13:14:48 GMT 2025
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