Top 10 R Packages to be a Kaggle Champion

我会带着你远行 2022-06-18 03:59 217阅读 0赞

By [Anmol Rajpurohit][].

![R][] Across all major surveys, R has clearly dominated as one of the top programming choices for data scientists. Thus, it is no wonder that knowing the important R packages can be a vital advantage in Kaggle competitions. Xavier Conort (currently Data Scientist at Data Robot) has compiled a list of 10 R packages that played a key role in getting a top 10 ranking in more than 15 Kaggle competitions (including winning a few of them).   
  
Since R is widely being used even outside the data science community (such as by statisticians, actuaries, etc.), this list of top 10 powerful R packages might help you in more ways than you might think.   
  
Here are those 10 packages particularly powerful to build winning solutions:

1.    
    Allowing the machine to capture complexity:  
    
2.  [gbm][] \[Gradient Boosting Machine\]
3.  [randomForest][] \[Random Forest\]
4.  [e1071][] \[Support Vector Machines\]  
    Taking advantage of high-cardinality categorical or text-data:  
    
5.  [glmnet][] \[Lasso and Elastic-Net Regularized Generalized Linear Models\]
6.  [tau][] \[Text Analysis Utilities\]  
    Making your code more efficient:  
    
7.  [Matrix][tau] \[Sparse and Dense Matrix Classes and Methods\]
8.  [SOAR][] \[Memory management in R by delayed assignments\]
9.  [foreach][] \[Foreach looping construct for R\]
10. [doMC][] \[Foreach parallel adaptor for the multicore package\]
11. [data.table][] \[Extension of data.frame\]

![kaggle][]  
   
Expert Advice for Kaggle Competitions:  Use your intuition to help the machine by doing the following:

*  Always compute differences/ratios of features
 *  Always consider discarding of features that are "too good"

The complete set of slides for this presentation by Xavier Conort:

[10 R Packages to Win Kaggle Competitions][] from  [DataRobot][]

[http://www.slideshare.net/DataRobot/final-10-r-xc-36610234][10 R Packages to Win Kaggle Competitions]    
  
Related:

*  [10 Steps to Success in Kaggle Data Science Competitions][]
 *  [7 common mistakes when doing Machine Learning][]
 *  [Introduction to Random Forests for Beginners – free ebook][Introduction to Random Forests for Beginners _ free ebook]

[Anmol Rajpurohit]: http://www.kdnuggets.com/author/anmol-rajpurohit
[R]: /images/20220618/3fa6882cee604a4f932359e5e50dbeaf.png
[gbm]: http://cran.r-project.org/web/packages/gbm/
[randomForest]: http://cran.r-project.org/web/packages/randomForest/
[e1071]: http://cran.r-project.org/web/packages/e1071/
[glmnet]: http://cran.r-project.org/web/packages/glmnet/
[tau]: http://cran.r-project.org/web/packages/Matrix/
[SOAR]: http://cran.r-project.org/web/packages/SOAR/
[foreach]: http://cran.r-project.org/web/packages/foreach/index.html
[doMC]: http://cran.r-project.org/web/packages/doMC/
[data.table]: http://cran.r-project.org/web/packages/data.table/index.html
[kaggle]: /images/20220618/9b0edf82e962433492ae38d4c7de9524.png
[10 R Packages to Win Kaggle Competitions]: http://www.slideshare.net/DataRobot/final-10-r-xc-36610234
[DataRobot]: http://www.slideshare.net/DataRobot
[10 Steps to Success in Kaggle Data Science Competitions]: http://www.kdnuggets.com/2015/03/10-steps-success-kaggle-data-science-competitions.html
[7 common mistakes when doing Machine Learning]: http://www.kdnuggets.com/2015/03/machine-learning-data-science-common-mistakes.html
[Introduction to Random Forests for Beginners _ free ebook]: http://www.kdnuggets.com/2014/03/introduction-random-forests-free-ebook.html