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  1. Visualization of MLP weights on MNIST — s...

    that is used to build this documentation on a regular basis. Iteration...
    scikit-learn.org/stable/auto_examples/neural_networks/plot_mnist_filters.html
    Fri Dec 05 17:52:55 GMT 2025
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  2. Customize crawler field values using an ingest ...

    to inspect a few documents. Go to the Documents tab and click on...section. Add a document to test your pipeline. Use a document from your...
    www.elastic.co/guide/en/enterprise-search/8.19/crawler-custom-values-ingest-pipeline.html
    Mon Oct 20 16:32:20 GMT 2025
      32.4K bytes
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  3. Dimensionality Reduction with Neighborhood Comp...

    Sample usage of Neighborhood Components Analysis for dimensionality reduction. This example compares different (linear) dimensionality reduction methods applied on the Digits data set. The data set...
    scikit-learn.org/stable/auto_examples/neighbors/plot_nca_dim_reduction.html
    Fri Dec 05 17:52:54 GMT 2025
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  4. Simple 1D Kernel Density Estimation — sci...

    This example uses the KernelDensity class to demonstrate the principles of Kernel Density Estimation in one dimension. The first plot shows one of the problems with using histograms to visualize th...
    scikit-learn.org/stable/auto_examples/neighbors/plot_kde_1d.html
    Fri Dec 05 17:52:54 GMT 2025
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  5. Using KBinsDiscretizer to discretize continuous...

    The example compares prediction result of linear regression (linear model) and decision tree (tree based model) with and without discretization of real-valued features. As is shown in the result be...
    scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization.html
    Fri Dec 05 17:52:54 GMT 2025
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  6. 1.4. Support Vector Machines — scikit-lea...

    Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection. The advantages of support vector machines are: Effective in high ...
    scikit-learn.org/stable/modules/svm.html
    Fri Dec 05 17:52:54 GMT 2025
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  7. L1 Penalty and Sparsity in Logistic Regression ...

    Comparison of the sparsity (percentage of zero coefficients) of solutions when L1, L2 and Elastic-Net penalty are used for different values of C. We can see that large values of C give more freedom...
    scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.html
    Fri Dec 05 17:52:55 GMT 2025
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  8. Regularization path of L1- Logistic Regression ...

    Train l1-penalized logistic regression models on a binary classification problem derived from the Iris dataset. The models are ordered from strongest regularized to least regularized. The 4 coeffic...
    scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_path.html
    Fri Dec 05 17:52:54 GMT 2025
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  9. Feature agglomeration vs. univariate selection ...

    This example compares 2 dimensionality reduction strategies: univariate feature selection with Anova, feature agglomeration with Ward hierarchical clustering. Both methods are compared in a regress...
    scikit-learn.org/stable/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection....
    Fri Dec 05 17:52:55 GMT 2025
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  10. Online learning of a dictionary of parts of fac...

    This example uses a large dataset of faces to learn a set of 20 x 20 images patches that constitute faces. From the programming standpoint, it is interesting because it shows how to use the online ...
    scikit-learn.org/stable/auto_examples/cluster/plot_dict_face_patches.html
    Fri Dec 05 17:52:55 GMT 2025
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