Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Fix for CRAN Submission (#1826)

Browse Source 
* fix cran check

* change required R version because of utils::globalVariables

* temporary commit, monotone not working

* fix test

* fix doc

* fix doc

* fix cran note and warning

* improve checks

* fix urls
This commit is contained in:
Tong He
2016-12-02 20:19:03 -08:00
committed by Tianqi Chen
parent 27ca50e2c2
commit 2f3958a455
15 changed files with 40 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
R-package/vignettes/xgboostPresentation.Rmd
View File

@@ -57,7 +57,7 @@ drat:::addRepo("dmlc")
install.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source")
```
> *Windows* user will need to install [Rtools](http://cran.r-project.org/bin/windows/Rtools/) first.
> *Windows* user will need to install [Rtools](https://cran.r-project.org/bin/windows/Rtools/) first.
### CRAN version
@@ -68,7 +68,7 @@ The version 0.4-2 is on CRAN, and you can install it by:
install.packages("xgboost")
```
Formerly available versions can be obtained from the CRAN [archive](http://cran.r-project.org/src/contrib/Archive/xgboost)
Formerly available versions can be obtained from the CRAN [archive](https://cran.r-project.org/src/contrib/Archive/xgboost)
## Learning
@@ -107,7 +107,7 @@ train <- agaricus.train
test <- agaricus.test
```
> In the real world, it would be up to you to make this division between `train` and `test` data. The way to do it is out of the purpose of this article, however `caret` package may [help](http://topepo.github.io/caret/splitting.html).
> In the real world, it would be up to you to make this division between `train` and `test` data. The way to do it is out of the purpose of this article, however `caret` package may [help](http://topepo.github.io/caret/data-splitting.html).
Each variable is a `list` containing two things, `label` and `data`:
@@ -294,7 +294,7 @@ bst <- xgb.train(data=dtrain, max_depth=2, eta=1, nthread = 2, nrounds=2, watchl
Both training and test error related metrics are very similar, and in some way, it makes sense: what we have learned from the training dataset matches the observations from the test dataset.
If with your own dataset you have not such results, you should think about how you divided your dataset in training and test. May be there is something to fix. Again, `caret` package may [help](http://topepo.github.io/caret/splitting.html).
If with your own dataset you have not such results, you should think about how you divided your dataset in training and test. May be there is something to fix. Again, `caret` package may [help](http://topepo.github.io/caret/data-splitting.html).
For a better understanding of the learning progression, you may want to have some specific metric or even use multiple evaluation metrics.