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R maintenance Feb2017 (#2045)

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* [R] better argument check in xgb.DMatrix; fixes #1480

* [R] showsd was a dummy; fixes #2044

* [R] better categorical encoding explanation in vignette; fixes #1989

* [R] new roxygen version docs update
This commit is contained in:
Vadim Khotilovich
2017-02-20 12:02:40 -06:00
committed by Tianqi Chen
parent 63aec12a13
commit b4d97d3cb8
42 changed files with 19 additions and 48 deletions
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11
R-package/vignettes/discoverYourData.Rmd
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@@ -134,23 +134,24 @@ levels(df[,Treatment])
```
#### One-hot encoding
#### Encoding categorical features
Next step, we will transform the categorical data to dummy variables.
This is the [one-hot encoding](http://en.wikipedia.org/wiki/One-hot) step.
Several encoding methods exist, e.g., [one-hot encoding](http://en.wikipedia.org/wiki/One-hot) is a common approach.
We will use the [dummy contrast coding](http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm#dummy) which is popular because it producess "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}`.
For example, the column `Treatment` will be replaced by two columns, `Placebo`, and `Treated`. Each of them will be *binary*. Therefore, an observation which has the value `Placebo` in column `Treatment` before the transformation will have after the transformation the value `1` in the new column `Placebo` and the value `0` in the new column `Treated`. The column `Treatment` will disappear during the one-hot encoding.
For example, the column `Treatment` will be replaced by two columns, `TreatmentPlacebo`, and `TreatmentTreated`. Each of them will be *binary*. Therefore, an observation which has the value `Placebo` in column `Treatment` before the transformation will have after the transformation the value `1` in the new column `TreatmentPlacebo` and the value `0` in the new column `TreatmentTreated`. The column `TreatmentPlacebo` will disappear during the contrast encoding, as it would be absorbed into a common constant intercept column.
Column `Improved` is excluded because it will be our `label` column, the one we want to predict.
```{r, warning=FALSE,message=FALSE}
sparse_matrix <- sparse.model.matrix(Improved~.-1, data = df)
sparse_matrix <- sparse.model.matrix(Improved ~ ., data = df)[,-1]
head(sparse_matrix)
```
> Formulae `Improved~.-1` used above means transform all *categorical* features but column `Improved` to binary values. The `-1` is here to remove the first column which is full of `1` (this column is generated by the conversion). For more information, you can type `?sparse.model.matrix` in the console.
> Formula `Improved ~ .` used above means transform all *categorical* features but column `Improved` to binary values. The `-1` column selection removes the intercept column which is full of `1` (this column is generated by the conversion). For more information, you can type `?sparse.model.matrix` in the console.
Create the output `numeric` vector (not as a sparse `Matrix`):