Pushing the docs to dev/ for branch: master, commit ca08cd5016d843dabf0e52b7479c936f2a724753

Author: sklearn-ci

dev/_downloads/3162dbcc22e3e4dcce68f4934b2c332f/plot_map_data_to_normal.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Map data to a normal distribution\n\n\nThis example demonstrates the use of the Box-Cox and Yeo-Johnson transforms\nthrough :class:`preprocessing.PowerTransformer` to map data from various\ndistributions to a normal distribution.\n\nThe power transform is useful as a transformation in modeling problems where\nhomoscedasticity and normality are desired. Below are examples of Box-Cox and\nYeo-Johnwon applied to six different probability distributions: Lognormal,\nChi-squared, Weibull, Gaussian, Uniform, and Bimodal.\n\nNote that the transformations successfully map the data to a normal\ndistribution when applied to certain datasets, but are ineffective with others.\nThis highlights the importance of visualizing the data before and after\ntransformation.\n\nAlso note that even though Box-Cox seems to perform better than Yeo-Johnson for\nlognormal and chi-squared distributions, keep in mind that Box-Cox does not\nsupport inputs with negative values.\n\nFor comparison, we also add the output from\n:class:`preprocessing.QuantileTransformer`. It can force any arbitrary\ndistribution into a gaussian, provided that there are enough training samples\n(thousands). Because it is a non-parametric method, it is harder to interpret\nthan the parametric ones (Box-Cox and Yeo-Johnson).\n\nOn \"small\" datasets (less than a few hundred points), the quantile transformer\nis prone to overfitting. The use of the power transform is then recommended.\n"
+        "\n# Map data to a normal distribution\n\n\n.. currentmodule:: sklearn.preprocessing\n\nThis example demonstrates the use of the Box-Cox and Yeo-Johnson transforms\nthrough :class:`~PowerTransformer` to map data from various\ndistributions to a normal distribution.\n\nThe power transform is useful as a transformation in modeling problems where\nhomoscedasticity and normality are desired. Below are examples of Box-Cox and\nYeo-Johnwon applied to six different probability distributions: Lognormal,\nChi-squared, Weibull, Gaussian, Uniform, and Bimodal.\n\nNote that the transformations successfully map the data to a normal\ndistribution when applied to certain datasets, but are ineffective with others.\nThis highlights the importance of visualizing the data before and after\ntransformation.\n\nAlso note that even though Box-Cox seems to perform better than Yeo-Johnson for\nlognormal and chi-squared distributions, keep in mind that Box-Cox does not\nsupport inputs with negative values.\n\nFor comparison, we also add the output from\n:class:`~QuantileTransformer`. It can force any arbitrary\ndistribution into a gaussian, provided that there are enough training samples\n(thousands). Because it is a non-parametric method, it is harder to interpret\nthan the parametric ones (Box-Cox and Yeo-Johnson).\n\nOn \"small\" datasets (less than a few hundred points), the quantile transformer\nis prone to overfitting. The use of the power transform is then recommended.\n"
       ]
     },
     {

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -3,8 +3,10 @@
 Map data to a normal distribution
 =================================
 
+.. currentmodule:: sklearn.preprocessing
+
 This example demonstrates the use of the Box-Cox and Yeo-Johnson transforms
-through :class:`preprocessing.PowerTransformer` to map data from various
+through :class:`~PowerTransformer` to map data from various
 distributions to a normal distribution.
 
 The power transform is useful as a transformation in modeling problems where
@@ -22,7 +24,7 @@
 support inputs with negative values.
 
 For comparison, we also add the output from
-:class:`preprocessing.QuantileTransformer`. It can force any arbitrary
+:class:`~QuantileTransformer`. It can force any arbitrary
 distribution into a gaussian, provided that there are enough training samples
 (thousands). Because it is a non-parametric method, it is harder to interpret
 than the parametric ones (Box-Cox and Yeo-Johnson).