Weighting in Conditional Tree and SVM


R Data Science: R Programming A-Z: R For Data Science With Real Exercises!

When there is a problem of class imbalances, it is important to apply weights to fine tune model performance.

Conditional Inference Tree / Forest
ct1 <- ctree(Class ~ ., data=mydata, weights= ifelse(mydata$Class=='churn', 10, 1),mincriterion = 0.999)
It means giving more importance to correct classification of churn than non-attritors. The same weights function can be applied to cforest.

Support Vector Machine

Add class.weights option in SVM.
class.weights = c(churn= 10, non-attritors = 1)
Coursera Data Science

R Tutorials : 75 Free R Tutorials

Get Free Email Updates :
*Please confirm your email address by clicking on the link sent to your Email*

Related Posts:

0 Response to "Weighting in Conditional Tree and SVM"

Post a Comment

Next → ← Prev