What is the difference between softprob and softmax in Xgboost

@hackers wrote:

Hello,

While trying to implement Xgboost in R,I came across the code:

param <- list("objective" = "multi:softprob",    # multiclass classification 
              "num_class" = 12,    # number of classes 
              "eval_metric" = "merror",    # evaluation metric 
              "nthread" = 6,   # number of threads to be used 
              "max_depth" = 15,    # maximum depth of tree 
              "eta" = 0.07,    # step size shrinkage 
              "subsample" = 0.8,    # part of data instances to grow tree 
              "colsample_bytree" = 0.9  # subsample ratio of columns when constructing each tree 
              
)

Here for multiclass we can use either softprob or softmax.
Can someone help me in understanding the difference between the two??

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