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Sample R code for
Random Forest.

library(randomForest)

rf50 <- randomForest(Creditability ~., data = Train50, ntree=200, importance=T, proximity=T)

plot(rf50, main="")

rf50

Test50_rf_pred <- predict(rf50, Test50, type="class")

table(Test50_rf_pred, Test50$Creditability)

importance(rf50)

varImpPlot(rf50,  main="", cex=0.8)

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

Importance of predictors are given in the following dotplot.

which gives rise to the following classification table:

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.