Weighting in Conditional Tree and SVM

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)
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Deepanshu founded ListenData with a simple objective - Make analytics easy to understand and follow. He has over 10 years of experience in data science. During his tenure, he has worked with global clients in various domains like Banking, Insurance, Private Equity, Telecom and Human Resource.

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