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Merge pull request #350 from jeremyatia/patch-1

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Update understandingXGBoostModel.Rmd
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Tianqi Chen 2015-06-08 16:36:40 -07:00
commit 00a8076deb
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demo/kaggle-otto/understandingXGBoostModel.Rmd
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@ -45,7 +45,7 @@ dim(train)
train[1:6,1:5, with =F] train[1:6,1:5, with =F]
# Test dataset dimensions # Test dataset dimensions
dim(train) dim(test)
# Test content # Test content
test[1:6,1:5, with =F] test[1:6,1:5, with =F]
@ -228,4 +228,4 @@ There are 4 documents you may also be interested in:
* [xgboostPresentation.Rmd](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd): general presentation * [xgboostPresentation.Rmd](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd): general presentation
* [discoverYourData.Rmd](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/discoverYourData.Rmd): explaining feature analysus * [discoverYourData.Rmd](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/discoverYourData.Rmd): explaining feature analysus
* [Feature Importance Analysis with XGBoost in Tax audit](http://fr.slideshare.net/MichaelBENESTY/feature-importance-analysis-with-xgboost-in-tax-audit): use case * [Feature Importance Analysis with XGBoost in Tax audit](http://fr.slideshare.net/MichaelBENESTY/feature-importance-analysis-with-xgboost-in-tax-audit): use case
* [The Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/): very good book to have a good understanding of the model * [The Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/): very good book to have a good understanding of the model