Nov 7, 2017 · What is the difference between xgb.train and xgb.XGBRegressor (or xgb.XGBClassifier)? Asked 8 years, 1 month ago Modified 3 years, 3 months ago Viewed 28k …

Sep 18, 2019 · I am trying out multi-class classification with xgboost and I've built it using this code, clf = xgb.XGBClassifier(max_depth=7, n_estimators=1000) clf.fit(byte_train, y_train) train1 …

Jan 8, 2016 · I am attempting to use XGBoosts classifier to classify some binary data. When I do the simplest thing and just use the defaults (as follows) clf = xgb.XGBClassifier() …

Nov 9, 2016 · I am using XGBoost cv to find the optimal number of rounds for my model. I would be very grateful if someone could confirm (or refute), the optimal number of rounds is: estop = …

Oct 18, 2023 · I have a dataset with prediction input dataset which consists of some generic columns, some feature columns and a label column. I also have an xgboost model of type …

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 the optimal …

Aug 13, 2021 · In your example with xgb, there are many hyper parameters eg (subsample, eta) to be specified, and to get a sense of how the parameters chosen perform on unseen data, we use …

Dec 26, 2015 · I would like to use the xgboost cv function to find the best parameters for my training data set. I am confused by the api. How do I find the best parameter? Is this similar to …

Mar 28, 2017 · i am trying to do hyperparemeter search with using scikit-learn's GridSearchCV on XGBoost. During gridsearch i'd like it to early stop, since it reduce search time drastically and …

Jun 4, 2016 · For anyone who comes across this issue while using xgb.XGBRegressor() the workaround I'm using is to keep the data in a pandas.DataFrame() or numpy.array() and not to …