"valid deviance" is nan for GBM model, What does this means and how to get rid of this?

Question

I am using Gradient boosting for classification. Though the result is improving but I am getting NaN in validdeviance.

Model = gbm.fit(
  x= x_Train ,
  y = y_Train ,
  distribution = "bernoulli",
  n.trees = GBM_NTREES ,
  shrinkage = GBM_SHRINKAGE ,
  interaction.depth = GBM_DEPTH ,
  n.minobsinnode = GBM_MINOBS ,
  verbose = TRUE
  )

Result

How to tune the parameter to get the validdeviance.

Answer 1

I had the same issue, strangely, we're few on this one ...

Adding train.fraction = 0.5 to the option list solves the issue (it seems there is no default value, and validdeviance is not computed without the train.fraction value explicitly mentioned).