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  1. Illustration of Gaussian process classification...

    This example illustrates GPC on XOR data. Compared are a stationary, isotropic kernel (RBF) and a non-stationary kernel (DotProduct). On this particular dataset, the DotProduct kernel obtains consi...
    scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_xor.html
    Mon Mar 23 20:39:21 UTC 2026
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  2. Comparison of LDA and PCA 2D projection of Iris...

    The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 attributes: sepal length, sepal width, petal length and petal width. Principal Component Analysis (PCA)...
    scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html
    Mon Mar 23 20:39:20 UTC 2026
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  3. t-SNE: The effect of various perplexity values ...

    An illustration of t-SNE on the two concentric circles and the S-curve datasets for different perplexity values. We observe a tendency towards clearer shapes as the perplexity value increases. The ...
    scikit-learn.org/stable/auto_examples/manifold/plot_t_sne_perplexity.html
    Mon Mar 23 20:39:22 UTC 2026
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  4. Illustration of prior and posterior Gaussian pr...

    This example illustrates the prior and posterior of a GaussianProcessRegressor with different kernels. Mean, standard deviation, and 5 samples are shown for both prior and posterior distributions. ...
    scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_prior_posterior.html
    Mon Mar 23 20:39:21 UTC 2026
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  5. Single estimator versus bagging: bias-variance ...

    This example illustrates and compares the bias-variance decomposition of the expected mean squared error of a single estimator against a bagging ensemble. In regression, the expected mean squared e...
    scikit-learn.org/stable/auto_examples/ensemble/plot_bias_variance.html
    Mon Mar 23 20:39:21 UTC 2026
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  6. Compressive sensing: tomography reconstruction ...

    This example shows the reconstruction of an image from a set of parallel projections, acquired along different angles. Such a dataset is acquired in computed tomography(CT). Without any prior infor...
    scikit-learn.org/stable/auto_examples/applications/plot_tomography_l1_reconstruction.html
    Mon Mar 23 20:39:20 UTC 2026
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  7. Recognizing hand-written digits — scikit-learn ...

    This example shows how scikit-learn can be used to recognize images of hand-written digits, from 0-9. Digits dataset: The digits dataset consists of 8x8 pixel images of digits. The images attribute...
    scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html
    Mon Mar 23 20:39:22 UTC 2026
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  8. Demo of HDBSCAN clustering algorithm — scikit-l...

    In this demo we will take a look at cluster.HDBSCAN from the perspective of generalizing the cluster.DBSCAN algorithm. We’ll compare both algorithms on specific datasets. Finally we’ll evaluate HDB...
    scikit-learn.org/stable/auto_examples/cluster/plot_hdbscan.html
    Mon Mar 23 20:39:22 UTC 2026
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  9. Density Estimation for a Gaussian mixture — sci...

    Plot the density estimation of a mixture of two Gaussians. Data is generated from two Gaussians with different centers and covariance matrices. Total running time of the script:(0 minutes 0.121 sec...
    scikit-learn.org/stable/auto_examples/mixture/plot_gmm_pdf.html
    Mon Mar 23 20:39:22 UTC 2026
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  10. Sparse coding with a precomputed dictionary — s...

    Transform a signal as a sparse combination of Ricker wavelets. This example visually compares different sparse coding methods using the SparseCoder estimator. The Ricker (also known as Mexican hat ...
    scikit-learn.org/stable/auto_examples/decomposition/plot_sparse_coding.html
    Mon Mar 23 20:39:20 UTC 2026
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