Pushing the docs to dev/ for branch: main, commit ee5a1b69d1dfa99635a10f0a5b54ec263cedf866

Author: sklearn-ci

dev/_downloads/023324c27491610e7c0ccff87c59abf9/plot_kernel_pca.py

@@ -152,7 +152,7 @@
 # :class:`~sklearn.decomposition.KernelPCA`.
 #
 # Indeed, :meth:`~sklearn.decomposition.KernelPCA.inverse_transform` cannot
-# rely on an analytical back-projection and thus an extact reconstruction.
+# rely on an analytical back-projection and thus an exact reconstruction.
 # Instead, a :class:`~sklearn.kernel_ridge.KernelRidge` is internally trained
 # to learn a mapping from the kernalized PCA basis to the original feature
 # space. This method therefore comes with an approximation introducing small

dev/_downloads/067cd5d39b097d2c49dd98f563dac13a/plot_iterative_imputer_variants_comparison.ipynb

@@ -15,7 +15,7 @@
       "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 shoud 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:`~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"
       ]
     },
     {

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -32,7 +32,7 @@
 #
 # We will derive a dataset from the Mauna Loa Observatory that collected air
 # samples. We are interested in estimating the concentration of CO2 and
-# extrapolate it for futher year. First, we load the original dataset available
+# extrapolate it for further year. First, we load the original dataset available
 # in OpenML.
 from sklearn.datasets import fetch_openml
 
@@ -208,7 +208,7 @@
 gaussian_process.kernel_
 
 # %%
-# Thus, most of the target signal, with the mean substracted, is explained by a
+# Thus, most of the target signal, with the mean subtracted, is explained by a
 # long-term rising trend for ~45 ppm and a length-scale of ~52 years. The
 # periodic component has an amplitude of ~2.6ppm, a decay time of ~90 years and
 # a length-scale of ~1.5. The long decay time indicates that we have a

dev/_downloads/1b8827af01c9a70017a4739bcf2e21a8/plot_gpr_co2.py

@@ -58,7 +58,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Computing the LLE and t-SNE embeddings, we find that LLE seems to unroll the\nSwiss Roll pretty effectively. t-SNE on the other hand, is able\nto preserve the general structure of the data, but, poorly represents the\ncontinous nature of our original data. Instead, it seems to unnecessarily\nclump sections of points together.\n\n"
+        "Computing the LLE and t-SNE embeddings, we find that LLE seems to unroll the\nSwiss Roll pretty effectively. t-SNE on the other hand, is able\nto preserve the general structure of the data, but, poorly represents the\ncontinuous nature of our original data. Instead, it seems to unnecessarily\nclump sections of points together.\n\n"
       ]
     },
     {

dev/_downloads/4c773264381a88c3d3933952c6040058/plot_swissroll.ipynb

@@ -80,7 +80,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "To be in line with the defintion in [ZHT2007]_, we need to rescale the\nAIC and the BIC. Indeed, Zou et al. are ignoring some constant terms\ncompared to the original definition of AIC derived from the maximum\nlog-likelihood of a linear model. You can refer to\n`mathematical detail section for the User Guide <lasso_lars_ic>`.\n\n"
+        "To be in line with the definition in [ZHT2007]_, we need to rescale the\nAIC and the BIC. Indeed, Zou et al. are ignoring some constant terms\ncompared to the original definition of AIC derived from the maximum\nlog-likelihood of a linear model. You can refer to\n`mathematical detail section for the User Guide <lasso_lars_ic>`.\n\n"
       ]
     },
     {

dev/_downloads/51833337bfc73d152b44902e5baa50ff/plot_lasso_lars_ic.ipynb

@@ -37,7 +37,7 @@
 :class:`~linear_model.BayesianRidge` and
 :class:`~ensemble.RandomForestRegressor` give the best results.
 
-It shoud be noted that some estimators such as
+It should be noted that some estimators such as
 :class:`~ensemble.HistGradientBoostingRegressor` can natively deal with
 missing features and are often recommended over building pipelines with
 complex and costly missing values imputation strategies.

dev/_downloads/54823a4305997fc1281f34ce676fb43e/plot_iterative_imputer_variants_comparison.py

@@ -76,7 +76,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Displaying a Pipeline Chaining Multiple Preprocessing Steps & Classifier\n This section constructs a :class:`~sklearn.pipeline.Pipeline` with multiple\n preprocessing steps, :class:`~sklearn.preprocessing.PolynomialFeatures` and\n :class:`~sklearn.preprocessing.StandardScaler`, and a classifer step,\n :class:`~sklearn.linear_model.LogisticRegression`, and displays its visual\n representation.\n\n"
+        "## Displaying a Pipeline Chaining Multiple Preprocessing Steps & Classifier\n This section constructs a :class:`~sklearn.pipeline.Pipeline` with multiple\n preprocessing steps, :class:`~sklearn.preprocessing.PolynomialFeatures` and\n :class:`~sklearn.preprocessing.StandardScaler`, and a classifier step,\n :class:`~sklearn.linear_model.LogisticRegression`, and displays its visual\n representation.\n\n"
       ]
     },
     {

dev/_downloads/6ebaebc92484478ceb119d24fe9df21c/plot_pipeline_display.ipynb

@@ -37,7 +37,7 @@
 # Computing the LLE and t-SNE embeddings, we find that LLE seems to unroll the
 # Swiss Roll pretty effectively. t-SNE on the other hand, is able
 # to preserve the general structure of the data, but, poorly represents the
-# continous nature of our original data. Instead, it seems to unnecessarily
+# continuous nature of our original data. Instead, it seems to unnecessarily
 # clump sections of points together.
 
 sr_lle, sr_err = manifold.locally_linear_embedding(