Pushing the docs to 1.3/ for branch: 1.3.X, commit 55a65a2fa5653257225d7e184da3d0c00ff852b1

Author: sklearn-ci

1.3/.buildinfo

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 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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+config: 75bbb5dd8822aca30c6a475439414f41
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1.3/_downloads/006fc185672e58b056a5c134db26935c/plot_coin_segmentation.ipynb

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1.3/_downloads/023324c27491610e7c0ccff87c59abf9/plot_kernel_pca.py

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 ==========
 
 This example shows the difference between the Principal Components Analysis
-(:class:`~sklearn.decomposition.PCA`) and its kernalized version
+(:class:`~sklearn.decomposition.PCA`) and its kernelized version
 (:class:`~sklearn.decomposition.KernelPCA`).
 
 On the one hand, we show that :class:`~sklearn.decomposition.KernelPCA` is able

1.3/_downloads/02f111fb3dd79805b161e14c564184fc/plot_sgd_comparison.ipynb

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1.3/_downloads/0486bf9e537e44cedd2a236d034bcd90/plot_pcr_vs_pls.ipynb

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1.3/_downloads/055e8313e28f2f3b5fd508054dfe5fe0/plot_roc_crossval.ipynb

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1.3/_downloads/05ca8a4e90b4cc2acd69f9e24b4a1f3a/plot_classifier_chain_yeast.ipynb

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1.3/_downloads/061854726c268bcdae5cd1c330cf8c75/plot_sgd_penalties.ipynb

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1.3/_downloads/067cd5d39b097d2c49dd98f563dac13a/plot_iterative_imputer_variants_comparison.ipynb

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       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Imputing missing values with variants of IterativeImputer\n\n.. currentmodule:: sklearn\n\nThe :class:`~impute.IterativeImputer` class is very flexible - it can be\nused with a variety of estimators to do round-robin regression, treating every\nvariable as an output in turn.\n\nIn this example we compare some estimators for the purpose of missing feature\nimputation with :class:`~impute.IterativeImputer`:\n\n* :class:`~linear_model.BayesianRidge`: regularized linear regression\n* :class:`~tree.RandomForestRegressor`: Forests of randomized trees regression\n* :func:`~pipeline.make_pipeline`(:class:`~kernel_approximation.Nystroem`,\n  :class:`~linear_model.Ridge`): a pipeline with the expansion of a degree 2\n  polynomial kernel and regularized linear regression\n* :class:`~neighbors.KNeighborsRegressor`: comparable to other KNN\n  imputation approaches\n\nOf particular interest is the ability of\n:class:`~impute.IterativeImputer` to mimic the behavior of missForest, a\npopular imputation package for R.\n\nNote that :class:`~neighbors.KNeighborsRegressor` is different from KNN\nimputation, which learns from samples with missing values by using a distance\nmetric that accounts for missing values, rather than imputing them.\n\nThe goal is to compare different estimators to see which one is best for the\n:class:`~impute.IterativeImputer` when using a\n:class:`~linear_model.BayesianRidge` estimator on the California housing\ndataset with a single value randomly removed from each row.\n\nFor this particular pattern of missing values we see that\n:class:`~linear_model.BayesianRidge` and\n:class:`~ensemble.RandomForestRegressor` give the best results.\n\nIt should be noted that some estimators such as\n:class:`~ensemble.HistGradientBoostingRegressor` can natively deal with\nmissing features and are often recommended over building pipelines with\ncomplex and costly missing values imputation strategies.\n"
+        "\n# Imputing missing values with variants of IterativeImputer\n\n.. currentmodule:: sklearn\n\nThe :class:`~impute.IterativeImputer` class is very flexible - it can be\nused with a variety of estimators to do round-robin regression, treating every\nvariable as an output in turn.\n\nIn this example we compare some estimators for the purpose of missing feature\nimputation with :class:`~impute.IterativeImputer`:\n\n* :class:`~linear_model.BayesianRidge`: regularized linear regression\n* :class:`~ensemble.RandomForestRegressor`: Forests of randomized trees regression\n* :func:`~pipeline.make_pipeline` (:class:`~kernel_approximation.Nystroem`,\n  :class:`~linear_model.Ridge`): a pipeline with the expansion of a degree 2\n  polynomial kernel and regularized linear regression\n* :class:`~neighbors.KNeighborsRegressor`: comparable to other KNN\n  imputation approaches\n\nOf particular interest is the ability of\n:class:`~impute.IterativeImputer` to mimic the behavior of missForest, a\npopular imputation package for R.\n\nNote that :class:`~neighbors.KNeighborsRegressor` is different from KNN\nimputation, which learns from samples with missing values by using a distance\nmetric that accounts for missing values, rather than imputing them.\n\nThe goal is to compare different estimators to see which one is best for the\n:class:`~impute.IterativeImputer` when using a\n:class:`~linear_model.BayesianRidge` estimator on the California housing\ndataset with a single value randomly removed from each row.\n\nFor this particular pattern of missing values we see that\n:class:`~linear_model.BayesianRidge` and\n:class:`~ensemble.RandomForestRegressor` give the best results.\n\nIt should be noted that some estimators such as\n:class:`~ensemble.HistGradientBoostingRegressor` can natively deal with\nmissing features and are often recommended over building pipelines with\ncomplex and costly missing values imputation strategies.\n"
       ]
     },
     {
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       "pygments_lexer": "ipython3",
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1.3/_downloads/06cfc926acb27652fb2aa5bfc583e7cb/plot_hashing_vs_dict_vectorizer.ipynb

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