How to impute missing values in a dataset before feeding to a classifier is often a difficult decision. Imputing with a wrong value can significantly skew the data and result in wrong classifier. The ideal solution is to get a clean data set without any NULL values but then, we might have to throw out most data. There are no perfect workarounds as most classifiers are built based on the information from data and lack thereof results in the wrong classifier. Continue reading “Handling missing values in a Dataset before training”
Although there are multiple packages which plots ROC curve, this one seems to be the most convenient.
library(caTools) # Predict on test: p p <- predict(model, test, type = "response") # create ROC Curve colAUC(p,test[["Class"]],plotROC = T)