diff --git a/docs/articles/tutorial/feature_selection.html b/docs/articles/tutorial/feature_selection.html index 27bc2202a8..484de1ec6f 100644 --- a/docs/articles/tutorial/feature_selection.html +++ b/docs/articles/tutorial/feature_selection.html @@ -344,7 +344,7 @@

## 7 Petal.Length numeric FSelector_chi.squared 0.9346311 ## 8 Petal.Width numeric FSelector_chi.squared 0.9432359

A bar plot of importance values for the individual features can be obtained using function plotFilterValues().

-
plotFilterValues(fv2) + ggpubr::theme_pubr()
+
plotFilterValues(fv2) + ggpubr::theme_pubr()

By default plotFilterValues() will create facetted subplots if multiple filter methods are passed as input to generateFilterValuesData().

According to the "information.gain" measure, Petal.Width and Petal.Length contain the most information about the target variable Species.

diff --git a/docs/news/index.html b/docs/news/index.html index fd37a21aa0..602d17f46a 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -385,8 +385,6 @@

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  • See also
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  • Examples
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