Top 10 R Packages to be a Kaggle Champion

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:

Allowing the machine to capture complexity:

1. gbm [Gradient Boosting Machine]
2. randomForest [Random Forest]
3. e1071 [Support Vector Machines]

Taking advantage of high-cardinality categorical or text-data:

4. glmnet [Lasso and Elastic-Net Regularized Generalized Linear Models]
5. tau [Text Analysis Utilities]

Making your code more efficient:

6. Matrix [Sparse and Dense Matrix Classes and Methods]
7. SOAR [Memory management in R by delayed assignments]
8. foreach [Foreach looping construct for R]
9. doMC [Foreach parallel adaptor for the multicore package]
10. data.table [Extension of data.frame]

 

 
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: http://www.slideshare.net/DataRobot/final-10-r-xc-36610234

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