Calculating Variable Importance with Random Forest

In random forest, you can calculate important variables with IMPORTANCE= TRUE parameter.

R Code : Variable Importance
library(caret)
rfTune <- train(dev[, -1], dev[,1], method = "rf", ntree = 100, importance = TRUE)

MeanDecreaseAccuracy table represents how much removing each variable reduces the accuracy of the model.

Selecting top 10 variables
ImportanceOrder <- order(rfTune$finalModel$importance[,1],decreasing = TRUE)
top10 <- rownames(rfTune$finalModel$importance[ImportanceOrder,])[1:10]
subsetimp <- subset(training, select = top10)
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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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