I'd like to identify the most predictive features for my classification model. I'm using this data.
Here is a sample.
This is my code. I'd like to use the predictors to predict loan status.
library(caret) folds <- 10 cvIndex <- createFolds(factor(Vc$Loan_Status), folds, returnTrain = T) tc <- trainControl(index = cvIndex, method = 'cv', number = folds) preProcValues <- preProcess(Vc, method = c("center","scale")) train_processed <- predict(preProcValues, Vc) train_processed$Loan_Status <- ifelse(train_processed$Loan_Status==FALSE,0,1) index <- createDataPartition(train_processed$quoteCreated, p=0.75, list=FALSE) trainSet <- train_processed[ index,] testSet <- train_processed[-index,] control <- rfeControl(functions = rfFuncs, method = "repeatedcv", repeats = 3, verbose = FALSE) outcomeName<-'Loan_Status' predictors<-names(trainSet)[!(names(trainSet) %in% outcomeName | names(trainSet) == "Loan_ID")] Quote_Pred_Profile <- rfe(trainSet[,predictors], trainSet[,outcomeName], metric = "Accuracy", maximizize = TRUE, rfeControl = control) plot(Quote_Pred_Profile)
I get the warning message:
In randomForest.default(x, y, importance = TRUE, ...) : The response has five or fewer unique values. Are you sure you want to do regression?
Why is it serving this error? Is this the right way to do subset selection to prep for classification?
Also, when I run
plot(Quote_Pred_Profile), it serves a plot with RMSE on the y-axis.