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  1. Cross decomposition — scikit-learn 1.7.0 docume...

    Examples concerning the sklearn.cross_decomposition module. Compare cross decomposition methods Principal Component Regression vs Partial Least Squares Regression
    scikit-learn.org/stable/auto_examples/cross_decomposition/index.html
    Mon Jul 07 14:36:32 UTC 2025
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  2. Inductive Clustering — scikit-learn 1.7.0 docum...

    Clustering can be expensive, especially when our dataset contains millions of datapoints. Many clustering algorithms are not inductive and so cannot be directly applied to new data samples without ...
    scikit-learn.org/stable/auto_examples/cluster/plot_inductive_clustering.html
    Mon Jul 07 14:36:35 UTC 2025
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  3. Nearest Neighbors — scikit-learn 1.7.0 document...

    Examples concerning the sklearn.neighbors module. Approximate nearest neighbors in TSNE Caching nearest neighbors Comparing Nearest Neighbors with and without Neighborhood Components Analysis Dimen...
    scikit-learn.org/stable/auto_examples/neighbors/index.html
    Mon Jul 07 14:36:35 UTC 2025
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  4. Quantile regression — scikit-learn 1.7.0 docume...

    This example illustrates how quantile regression can predict non-trivial conditional quantiles. The left figure shows the case when the error distribution is normal, but has non-constant variance, ...
    scikit-learn.org/stable/auto_examples/linear_model/plot_quantile_regression.html
    Mon Jul 07 14:36:32 UTC 2025
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  5. Analyzing online search relevance metrics with ...

    single user searching for documentation on elastic.co (note that...Query ID: qid-001 Position: 2 Document ID: https://www.elastic.c...
    www.elastic.co/blog/analyzing-online-search-relevance-metrics-with-the-elastic-stack
    Tue Jul 08 00:36:44 UTC 2025
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  6. 2.8. Density Estimation — scikit-learn 1.7.0 do...

    Density estimation walks the line between unsupervised learning, feature engineering, and data modeling. Some of the most popular and useful density estimation techniques are mixture models such as...
    scikit-learn.org/stable/modules/density.html
    Mon Jul 07 14:36:34 UTC 2025
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  7. Model-based and sequential feature selection — ...

    This example illustrates and compares two approaches for feature selection: SelectFromModel which is based on feature importance, and SequentialFeatureSelector which relies on a greedy approach. We...
    scikit-learn.org/stable/auto_examples/feature_selection/plot_select_from_model_diabetes.html
    Mon Jul 07 14:36:35 UTC 2025
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  8. Ledoit-Wolf vs OAS estimation — scikit-learn 1....

    The usual covariance maximum likelihood estimate can be regularized using shrinkage. Ledoit and Wolf proposed a close formula to compute the asymptotically optimal shrinkage parameter (minimizing a...
    scikit-learn.org/stable/auto_examples/covariance/plot_lw_vs_oas.html
    Mon Jul 07 14:36:32 UTC 2025
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  9. A demo of the mean-shift clustering algorithm —...

    Reference: Dorin Comaniciu and Peter Meer, “Mean Shift: A robust approach toward feature space analysis”. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002. pp. 603-619. Generate...
    scikit-learn.org/stable/auto_examples/cluster/plot_mean_shift.html
    Mon Jul 07 14:36:35 UTC 2025
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  10. Plot different SVM classifiers in the iris data...

    Comparison of different linear SVM classifiers on a 2D projection of the iris dataset. We only consider the first 2 features of this dataset: Sepal length, Sepal width. This example shows how to pl...
    scikit-learn.org/stable/auto_examples/svm/plot_iris_svc.html
    Mon Jul 07 14:36:32 UTC 2025
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