What are different options for objective functions available in xgboost.XGBClassifier?
That's true that binary:logistic is the default objective for XGBClassifier, but I don't see any reason why you couldn't use other objectives offered by XGBoost package.For example, you can see in sklearn.py source code that multi:softprob is used explicitly in multiclass case.
Moreover, if it's really necessary, you can provide a custom objective function (details here).
The default objective for XGBClassifier is ['reg:linear]however there are other parameters as well..binary:logistic-It returns predicted probabilities for predicted classmulti:softmax - Returns hard class for multiclass classificationmulti:softprob - It Returns probabilities for multiclass classification
Note: when using multi:softmax as objective, you need to pass num_class alsoas num_class is number of parameters defining number of classsuch as for labelliing (0,1,2), here we have 3 classes, so num_class = 3