CODEWITHSUNDEEP
rmachine-learningxgboostgbmboosting
Amarjeet
Amarjeet

Reputation: 925

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

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

enter image description here

How to tune the parameter to get the validdeviance.

Upvotes: 11

Views: 4836

Answers (1)

citraL
citraL

Reputation: 1156

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).

Upvotes: 14

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