XGBoost is a library for constructing boosted tree models in R, Python, Java, Scala, and C++. Use this tag for issues specific to the package (i.e. input/output, installation, functionality).
In the documentation of xgboost I read: base_score [default=0.5] : the initial prediction score of all instances, global bias What …
parameters xgboostI'm trying to use XGBoost, and optimize the eval_metric as auc(as described here). This works fine when using …
python scikit-learn classification pipeline xgboostI'm trying to know which loss function uses XGBoost for multi-class classification. I found in this question the loss function …
xgboost multiclass-classificationRecently, I am doing multiple experiments to compare Python XgBoost and LightGBM. It seems that this LightGBM is a new …
python cross-validation xgboost grid-search lightgbmHow can I change the figure size of xgboost's plot importance function? Trying to pass a figsize=(10,20) fails with the …
python matplotlib scikit-learn xgboosti'm trying to run a very simple example where XGBoost takes some data and do a binary classification. The documentation …
python machine-learning classification xgboost predictI would like to add the kappa evaluation metric to use in xgboost in Python. I am having trouble understanding …
machine-learning xgboost objective-functionWhat is the difference between objective and feval in xgboost in R? I know this is something very fundamental but …
r classification xgboost objective-function evaluation-functionI use the xgboots sklearn interface below to create and train an xgb model-1. clf = xgb.XGBClassifier(n_estimators = 100, objective= …
python scikit-learn xgboost