I tried to install XGBoost package in python. I am using windows os, 64bits . I have gone through following.
The package directory states that xgboost is unstable for windows and... moreI tried to install XGBoost package in python. I am using windows os, 64bits . I have gone through following.
The package directory states that xgboost is unstable for windows and is disabled: pip installation on windows is currently disabled for further invesigation, please install from github. https://pypi.python.org/pypi/xgboost/
I am not well versed in Visual Studio, facing problem building XGBoost. I am missing opportunities to utilize xgboost package in data science.
Please guide, so that I can import the XGBoost package in python.
Thanks less
I tried to install XGBoost package in python. I am using windows os, 64bits . I have gone through following.
The package directory states that xgboost is unstable for windows and... moreI tried to install XGBoost package in python. I am using windows os, 64bits . I have gone through following.
The package directory states that xgboost is unstable for windows and is disabled: pip installation on windows is currently disabled for further invesigation, please install from github. https://pypi.python.org/pypi/xgboost/
I am not well versed in Visual Studio, facing problem building XGBoost. I am missing opportunities to utilize xgboost package in data science.
Please guide, so that I can import the XGBoost package in python.
Thanks
I've been exploring the xgboost package in R and went through several demos as well as tutorials but this still confuses me: after using xgb.cv to do cross validation, how does... moreI've been exploring the xgboost package in R and went through several demos as well as tutorials but this still confuses me: after using xgb.cv to do cross validation, how does the optimal parameters get passed to xgb.train? Or should I calculate the ideal parameters (such as nround, max.depth) based on the output of xgb.cv?
param <- list("objective" = "multi:softprob",
"eval_metric" = "mlogloss",
"num_class" = 12)
cv.nround <- 11
cv.nfold <- 5
mdcv <-xgb.cv(data=dtrain,params = param,nthread=6,nfold = cv.nfold,nrounds = cv.nround,verbose = T)
md <-xgb.train(data=dtrain,params = param,nround = 80,watchlist = list(train=dtrain,test=dtest),nthread=6) less