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

ENH: More comments and explanation on demo using xgboost from caret

Browse Source
This commit is contained in:
terrytangyuan 2015-09-10 23:41:36 -04:00
parent 62e95dcc60
commit 424bcc05fa
1 changed files with 7 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
R-package/demo/caret_wrapper.R
View File

@ -22,11 +22,14 @@ df[,AgeCat:= as.factor(ifelse(Age > 30, "Old", "Young"))]
df[,ID:=NULL] df[,ID:=NULL]
#-------------Basic Training using XGBoost in caret Library----------------- #-------------Basic Training using XGBoost in caret Library-----------------
# set up control parameters for caret::train # Set up control parameters for caret::train
# here we use 10-fold cross-validation, repeating twice # Here we use 10-fold cross-validation, repeating twice, and using random search for tuning hyper-parameters.
fitControl <- trainControl(method = "cv", number = 10, repeats = 2) fitControl <- trainControl(method = "cv", number = 10, repeats = 2, search = "random")
# train a xgbTree model using caret::train # train a xgbTree model using caret::train
model <- train(factor(Improved)~., data = df, method = "xgbTree", trControl = fitControl) model <- train(factor(Improved)~., data = df, method = "xgbTree", trControl = fitControl)
# Instead of tree for our boosters, you can also fit a linear regression or logistic regression model using xgbLinear
# model <- train(factor(Improved)~., data = df, method = "xgbLinear", trControl = fitControl)
# See model results # See model results
print(model) print(model)