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pythonscikit-learnxgboost

xgboost.train versus XGBClassifier

I am using python to fit an xgboost model incrementally (chunk by chunk). I came across a solution that uses xgboost.train but I do not know what to do with the Booster object that it returns. For instance, the XGBClassifier has options like fit, predict, predict_proba etc.

Here is what happens inside the for loop that I am reading in the data little by little:

dtrain=xgb.DMatrix(X_train, label=y)
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
modelXG=xgb.train(param,dtrain,xgb_model='xgbmodel')
modelXG.save_model("xgbmodel")
Solution

  • XGBClassifier is a scikit-learn compatible class which can be used in conjunction with other scikit-learn utilities.

    Other than that, its just a wrapper over the xgb.train, in which you dont need to supply advanced objects like Booster etc.

    Just send your data to fit(), predict() etc and internally it will be converted to appropriate objects automatically.