DOC Fix broken link in plot_map_data_to_normal
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 @@ lognormal and chi-squared distributions, keep in mind that Box-Cox does not
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).
