Data Science Asked by honeybadger on February 3, 2021
I have a multiclass-classification dataset with the target (dependent) variable highly imbalanced. While using the randomForest package in R, I usually use the parameters sampsize & strata to account for the imbalance in training data. Are there any similar options in xgboost package also?
Summary of the number of datapoints available in each class.
Factor 1 : 667
Factor 2 : 676
Factor 3 :7807
Factor 4 : 850
In R, it's an option of the cross validation function : xgb.cv See the documentation here : https://www.rdocumentation.org/packages/xgboost/versions/0.4-4/topics/xgb.cv
Answered by lcrmorin on February 3, 2021
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