Multiclass classification with xgboost classifier?

Question

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         


        

Answer 1

By default, XGBClassifier uses the objective='binary:logistic'. When you use this objective, it employs either of these strategies: one-vs-rest (also known as one-vs-all) and one-vs-one. It may not be the right choice for your problem at hand.

When you use objective='multi:softprob', the output is a vector of number of data points * number of classes. As a result, there is an increase in time complexity of your code.

Try setting objective=multi:softmax in your code. It is more apt for multi-class classification task.