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[1.5.2] [R] Fix broken links. (#7675)

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Jiaming Yuan
2022-02-20 01:04:56 +08:00
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commit a09446d12b
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R-package/vignettes/discoverYourData.Rmd
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@@ -138,7 +138,7 @@ levels(df[,Treatment])
Next step, we will transform the categorical data to dummy variables.
Several encoding methods exist, e.g., [one-hot encoding](https://en.wikipedia.org/wiki/One-hot) is a common approach.
We will use the [dummy contrast coding](https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/) which is popular because it produces "full rank" encoding (also see [this blog post by Max Kuhn](http://appliedpredictivemodeling.com/blog/2013/10/23/the-basics-of-encoding-categorical-data-for-predictive-models)).
We will use the [dummy contrast coding](https://stats.oarc.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/) which is popular because it produces "full rank" encoding (also see [this blog post by Max Kuhn](http://appliedpredictivemodeling.com/blog/2013/10/23/the-basics-of-encoding-categorical-data-for-predictive-models)).
The purpose is to transform each value of each *categorical* feature into a *binary* feature `{0, 1}`.