softmax vs softprob in xgboost

[shashi-netra]

The xgboost documentation describes softmax and softprob thus:

multi:softmax: set XGBoost to do multiclass classification using the softmax objective, you also need to set num_class(number of classes)

multi:softprob: same as softmax, but output a vector of ndata * nclass, which can be further reshaped to ndata * nclass matrix. The result contains predicted probability of each data point belonging to each class.

I am confused about the difference since when I run the predict_proba method with multiple rows (ndata say),I do get a ndata * nclass matrix output as well. From what I know, softmax calculates probability distribution over a vector of values. So not sure what softprob is doing differently.

Can someone clarify the difference? It's probably very subtle but escapes me

[trivialfis]

softprob is outputting the probability for each class, softmax gives the most probable class id.