The document discusses using xgboost for machine learning and summarizes steps to prepare data for xgboost models. It recommends binding feature data together and writing it out in the libsvm format for efficient reading into an xgboost DMatrix object. It also suggests using the data.table package to write out libsvm files in parallel for improved performance on large datasets.

1. libsvm
2016/11/27
hskksk @ JapanR 2016

3. • :
• : R, Python, C++
• :
• :

4. xgboost kaggler

5. Bosch Production Line
Performance 15

6. xgboost

7. xgb.DMatrix

10. # feature
label = readRDS("label.rds")
feature_set_A = readRDS("feature_set_A.rds")
feature_set_B = readRDS("feature_set_B.rds")
# feature cbind
mat = cbind(
feature_set_A,
feature_set_B
)
↑
# DMatrix
dmat = xgb.DMatrix(mat, label=label)

11. cbind

12. ※cbind rm(vars); gc()

14. xgb.DMatrix

17. Python

18. libsvm
※R

19. 1. cbind libsvm
2. DMatrix

21. cbind libsvm
data.table::fwrite_libsvm(list_of_matrices, file)

22. data.table fork fwrite

23. # feature
label = readRDS("label.rds")
feature_set_A = fread("feature_set_A.csv")
feature_set_B = fread("feature_set_B.csv")
# feature list
# 1 label
matrices = list(label, feature_set_A, feature_set_B)
# libsvm
fwrite_libsvm(matrices, "libsvm.txt")
# DMatrix
dmat = xgb.DMatrix("libsvm.txt")

25. fwrite OpenMP
8.5GB/120sec @ Xeon 2.5GHz ✕ 8

26. data.table PR

27. https://github.com/hskksk/data.table
kaggler !!

28. Enjoy Kaggling with R !!