I am working on a multi-class classification task for 24 classes using XGBoost. I am training the model as follows:

param = {'max_depth': max_depth, 'eta': learning_rate, 'silent': 1, 'objective': 'multi:softmax', 'num_class': 24}
bst = xgb.train(param, dtrain, num_round)
bst = xgb.Booster({'nthread': 4})
predictions = bst.predict(dval)

I am getting the predictions as this array:

[ 0.5  0.5  0.5 ...,  0.5  0.5  0.5]

I don't know why it's predicting all classes as 0.5 instead of one of the indices in 0 to 23. Do I need to use any other objective parameter?

  • 2
    $\begingroup$ What is the line bst = xgb.Booster({'nthread': 4}) for? $\endgroup$
    – tagoma
    Nov 26 '17 at 21:16

I do not know python API, but I guess with the following line you overwrite the trained object with a freshly created booster object (0.5 is a default prediction)

bst = xgb.Booster({'nthread': 4})
  • $\begingroup$ Worked! I thought this would train using 4 threads. $\endgroup$
    – Hellboy
    Nov 27 '17 at 14:34

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