Pushing the docs to dev/ for branch: master, commit 175ded7caaeb1685ca29e26d9b7c7ee58467316d

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Imputing missing values with variants of IterativeImputer\n\n\nThe :class:`sklearn.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:`sklearn.imputeIterativeImputer`::\n\n    :class:`~sklearn.linear_model.BayesianRidge`: regularized linear regression\n    :class:`~sklearn.tree.DecisionTreeRegressor`: non-linear regression\n    :class:`~sklearn.ensemble.ExtraTreesRegressor`: similar to missForest in R\n    :class:`~sklearn.neighbors.KNeighborsRegressor`: comparable to other KNN\n    imputation approaches\n\nOf particular interest is the ability of\n:class:`sklearn.impute.IterativeImputer` to mimic the behavior of missForest, a\npopular imputation package for R. In this example, we have chosen to use\n:class:`sklearn.ensemble.ExtraTreesRegressor` instead of\n:class:`sklearn.ensemble.RandomForestRegressor` (as in missForest) due to its\nincreased speed.\n\nNote that :class:`sklearn.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:`sklearn.impute.IterativeImputer` when using a\n:class:`sklearn.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:`sklearn.ensemble.ExtraTreesRegressor` and\n:class:`sklearn.linear_model.BayesianRidge` give the best results.\n\n"
+        "\n# Imputing missing values with variants of IterativeImputer\n\n\nThe :class:`sklearn.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:`sklearn.imputeIterativeImputer`:\n\n* :class:`~sklearn.linear_model.BayesianRidge`: regularized linear regression\n* :class:`~sklearn.tree.DecisionTreeRegressor`: non-linear regression\n* :class:`~sklearn.ensemble.ExtraTreesRegressor`: similar to missForest in R\n* :class:`~sklearn.neighbors.KNeighborsRegressor`: comparable to other KNN\n  imputation approaches\n\nOf particular interest is the ability of\n:class:`sklearn.impute.IterativeImputer` to mimic the behavior of missForest, a\npopular imputation package for R. In this example, we have chosen to use\n:class:`sklearn.ensemble.ExtraTreesRegressor` instead of\n:class:`sklearn.ensemble.RandomForestRegressor` (as in missForest) due to its\nincreased speed.\n\nNote that :class:`sklearn.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:`sklearn.impute.IterativeImputer` when using a\n:class:`sklearn.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:`sklearn.ensemble.ExtraTreesRegressor` and\n:class:`sklearn.linear_model.BayesianRidge` give the best results.\n\n"
       ]
     },
     {

dev/_downloads/auto_examples_python.zip

@@ -8,13 +8,13 @@
 variable as an output in turn.
 
 In this example we compare some estimators for the purpose of missing feature
-imputation with :class:`sklearn.imputeIterativeImputer`::
+imputation with :class:`sklearn.imputeIterativeImputer`:
 
-    :class:`~sklearn.linear_model.BayesianRidge`: regularized linear regression
-    :class:`~sklearn.tree.DecisionTreeRegressor`: non-linear regression
-    :class:`~sklearn.ensemble.ExtraTreesRegressor`: similar to missForest in R
-    :class:`~sklearn.neighbors.KNeighborsRegressor`: comparable to other KNN
-    imputation approaches
+* :class:`~sklearn.linear_model.BayesianRidge`: regularized linear regression
+* :class:`~sklearn.tree.DecisionTreeRegressor`: non-linear regression
+* :class:`~sklearn.ensemble.ExtraTreesRegressor`: similar to missForest in R
+* :class:`~sklearn.neighbors.KNeighborsRegressor`: comparable to other KNN
+  imputation approaches
 
 Of particular interest is the ability of
 :class:`sklearn.impute.IterativeImputer` to mimic the behavior of missForest, a