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El Potaeto 2015-02-22 00:17:37 +01:00
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R-package/vignettes/vignette.css
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R-package/vignettes/xgboostPresentation.Rmd
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@ -22,8 +22,8 @@ This is an introductory document for using the \verb@xgboost@ package in *R*.
It is an efficient and scalable implementation of gradient boosting framework by @friedman2001greedy. Two solvers are included:
- *linear model*
- *tree learning* algorithm
- *linear* model ;
- *tree learning* algorithm.
It supports various objective functions, including *regression*, *classification* and *ranking*. The package is made to be extendible, so that users are also allowed to define their own objective function easily.
@ -48,7 +48,7 @@ Installation
The first step is to install the package.
For up-to-date version (which is *highly* recommended), install from Github:
For up-to-date version (which is *highly* recommended), install from *Github*:
```{r installGithub, eval=FALSE}
devtools::install_github('tqchen/xgboost',subdir='R-package')
@ -56,7 +56,7 @@ devtools::install_github('tqchen/xgboost',subdir='R-package')
> *Windows* user will need to install [RTools](http://cran.r-project.org/bin/windows/Rtools/) first.
For stable version on CRAN, run:
For stable version on *CRAN*, run:
```{r installCran, eval=FALSE}
install.packages('xgboost')
@ -194,11 +194,11 @@ print(paste("test-error=", err))
> We remind you that the algorithm has never seen the `test` data before.
Here, we have just computed a simple metric: the average error:
Here, we have just computed a simple metric, the average error.
* `as.numeric(pred > 0.5)` applies our rule that when the probability (== prediction == regression) is over `0.5` the observation is classified as `1` and `0` otherwise ;
* `probabilityVectorPreviouslyComputed != test$label` computes the vector of error between true data and computed probabilities ;
* `mean(vectorOfErrors)` computes the average error itself.
1. `as.numeric(pred > 0.5)` applies our rule that when the probability (== prediction == regression) is over `0.5` the observation is classified as `1` and `0` otherwise ;
2. `probabilityVectorPreviouslyComputed != test$label` computes the vector of error between true data and computed probabilities ;
3. `mean(vectorOfErrors)` computes the average error itself.
The most important thing to remember is that **to do a classification basically, you just do a regression and then apply a threeshold**.