Weighting in Conditional Tree and SVM

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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 close to 7 years of experience in data science and predictive modeling. During his tenure, he has worked with global clients in various domains like retail and commercial banking, Telecom, HR and Automotive.


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