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  1. Comparison of LDA and PCA 2D projection of Iris...

    1 , 2 ], target_names ): plt . scatter...y == i , 0 ], X_r [ y == i , 1 ], color = color , alpha = 0.8...
    scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html
    Fri Jun 06 09:13:47 UTC 2025
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  2. Restricted Boltzmann Machine features for digit...

    1 )) # 0-1 scaling X_train , X_test...False tol 1 C 100.0 fit_intercept True intercept_scaling 1 class_weight...
    scikit-learn.org/stable/auto_examples/neural_networks/plot_rbm_logistic_classification.html
    Fri Jun 06 09:13:47 UTC 2025
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  3. Factor Analysis (with rotation) to visualize pa...

    vmin =- 1 , vmax = 1 ) ax . set_xticks ([ 0 , 1 , 2 , 3 ])...set_xticks ([ 0 , 1 ]) ax . set_xticklabels ([ "Comp. 1" , "Comp. 2"...
    scikit-learn.org/stable/auto_examples/decomposition/plot_varimax_fa.html
    Fri Jun 06 09:13:47 UTC 2025
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  4. 5.2. Permutation feature importance — scikit-le...

    as: \[i_j = s - \frac{1}{K} \sum_{k=1}^{K} s_{k,j}\] 5.2.2. Relation...degradation of the model’s score [ 1 ] . By breaking the relationship...
    scikit-learn.org/stable/modules/permutation_importance.html
    Fri Jun 06 09:13:47 UTC 2025
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  5. Scikit-learn governance and decision-making — s...

    “about” page: Requires +1 by a maintainer, no -1 by a maintainer (lazy...changes require +1 by two maintainers, no -1 by a maintainer (lazy...
    scikit-learn.org/stable/governance.html
    Fri Jun 06 09:13:48 UTC 2025
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  6. An example of K-Means++ initialization — scikit...

    :: - 1 ] # Calculate seeds from k-means++...side sample data plt . figure ( 1 ) colors = [ "#4EACC5" , "#FF9C34"...
    scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_plusplus.html
    Fri Jun 06 09:13:47 UTC 2025
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  7. Comparison of kernel ridge and Gaussian process...

    num = 1_000 ) . reshape ( - 1 , 1 ) target = np . sin...ExpSineSquared(length_scale=1, periodicity=1) Our kernel has two parameters:...
    scikit-learn.org/stable/auto_examples/gaussian_process/plot_compare_gpr_krr.html
    Fri Jun 06 09:13:47 UTC 2025
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  8. Working with text documents — scikit-learn 1.7....

    Examples concerning the sklearn.feature_extraction.text module. Classification of text documents using sparse features Clustering text documents using k-means FeatureHasher and DictVectorizer Compa...
    scikit-learn.org/stable/auto_examples/text/index.html
    Fri Jun 06 09:13:47 UTC 2025
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  9. sklearn.impute — scikit-learn 1.7.0 documentation

    Transformers for missing value imputation. User guide. See the Imputation of missing values section for further details.
    scikit-learn.org/stable/api/sklearn.impute.html
    Fri Jun 06 09:13:47 UTC 2025
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  10. sklearn.compose — scikit-learn 1.7.0 documentation

    Meta-estimators for building composite models with transformers. In addition to its current contents, this module will eventually be home to refurbished versions of Pipeline and FeatureUnion. User ...
    scikit-learn.org/stable/api/sklearn.compose.html
    Fri Jun 06 09:13:47 UTC 2025
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