GCD.5 - Random Forest

  Sample R code for Random Forest

rf50 <- randomForest(Creditability ~., data = Train50, ntree=200, importance=T, proximity=T)
plot(rf50, main="")
Test50_rf_pred <- predict(rf50, Test50, type="class")
table(Test50_rf_pred, Test50$Creditability)
varImpPlot(rf50,  main="", cex=0.8)

Completely unsupervised random forest method on Training data with ntree = 200 leads to the following error plot:

error plot

Importance of predictors are given in the following dotplot.

dotplots of results

which gives rise to the following classification table:

table of results

With judicious choice of more important predictors, further improvement in accuracy is possible. But as improvement is slight, no attempt is made for supervised random forest.