Understanding num_classes for xgboost in R

Question

I\'m having a lot of trouble figuring out how to correctly set the num_classes for xgboost.

I\'ve got an example using the Iris data

df <- iris

         


        

Answer 1

I ran into this rather weird problem as well. It seemed in my class to be a result of not properly encoding the labels.

First, using a string vector with N classes as the labels, I could only get the algorithm to run by setting num_class = N + 1. However, this result was useless, because I only had N actual classes and N+1 buckets of predicted probabilities.

I re-encoded the labels as integers and then num_class worked fine when set to N.

# Convert classes to integers for xgboost
class <- data.table(interest_level=c("low", "medium", "high"), class=c(0,1,2))
t1    <- merge(t1, class, by="interest_level", all.x=TRUE, sort=F)

and

param <- list(booster="gbtree",
              objective="multi:softprob",
              eval_metric="mlogloss",
              #nthread=13,
              num_class=3,
              eta_decay = .99,
              eta = .005,
              gamma = 1,
              max_depth = 4,
              min_child_weight = .9,#1,
              subsample = .7,
              colsample_bytree = .5
)

For example.