Pushing the docs to dev/ for branch: main, commit b85377908bcad13fdbd275217a855ef1b1a60693

Author: sklearn-ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # 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.
-config: d987a56c84f07281051b030e9d3b7b74
+config: c345bc207d35274fa16c220abade560b
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -15,7 +15,7 @@
 KNeighbors models). The latter is demoed on the first part of the present
 example.
 
-On the second part of the example we show how Principle Component Analysis (PCA)
+On the second part of the example we show how Principal Component Analysis (PCA)
 is impacted by normalization of features. To illustrate this, we compare the
 principal components found using :class:`~sklearn.decomposition.PCA` on unscaled
 data with those obatined when using a

dev/_downloads/4ef6a0e5e8f2fe6463d63928373e5f91/plot_scaling_importance.py

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Importance of Feature Scaling\n\nFeature scaling through standardization, also called Z-score normalization, is\nan important preprocessing step for many machine learning algorithms. It\ninvolves rescaling each feature such that it has a standard deviation of 1 and a\nmean of 0.\n\nEven if tree based models are (almost) not affected by scaling, many other\nalgorithms require features to be normalized, often for different reasons: to\nease the convergence (such as a non-penalized logistic regression), to create a\ncompletely different model fit compared to the fit with unscaled data (such as\nKNeighbors models). The latter is demoed on the first part of the present\nexample.\n\nOn the second part of the example we show how Principle Component Analysis (PCA)\nis impacted by normalization of features. To illustrate this, we compare the\nprincipal components found using :class:`~sklearn.decomposition.PCA` on unscaled\ndata with those obatined when using a\n:class:`~sklearn.preprocessing.StandardScaler` to scale data first.\n\nIn the last part of the example we show the effect of the normalization on the\naccuracy of a model trained on PCA-reduced data.\n"
+        "\n# Importance of Feature Scaling\n\nFeature scaling through standardization, also called Z-score normalization, is\nan important preprocessing step for many machine learning algorithms. It\ninvolves rescaling each feature such that it has a standard deviation of 1 and a\nmean of 0.\n\nEven if tree based models are (almost) not affected by scaling, many other\nalgorithms require features to be normalized, often for different reasons: to\nease the convergence (such as a non-penalized logistic regression), to create a\ncompletely different model fit compared to the fit with unscaled data (such as\nKNeighbors models). The latter is demoed on the first part of the present\nexample.\n\nOn the second part of the example we show how Principal Component Analysis (PCA)\nis impacted by normalization of features. To illustrate this, we compare the\nprincipal components found using :class:`~sklearn.decomposition.PCA` on unscaled\ndata with those obatined when using a\n:class:`~sklearn.preprocessing.StandardScaler` to scale data first.\n\nIn the last part of the example we show the effect of the normalization on the\naccuracy of a model trained on PCA-reduced data.\n"
       ]
     },
     {