# R Language Basic examples - Classification and Regression

## Example

``````    ######  Used for both Classification and Regression examples
library(randomForest)
library(car)            ## For the Soils data
data(Soils)

######################################################
##    RF Classification Example
set.seed(656)            ## for reproducibility
S_RF_Class = randomForest(Gp ~ ., data=Soils[,c(4,6:14)])
Gp_RF = predict(S_RF_Class, Soils[,6:14])
length(which(Gp_RF != Soils\$Gp))            ## No Errors

## Naive Bayes for comparison
library(e1071)
S_NB  = naiveBayes(Soils[,6:14], Soils[,4])
Gp_NB = predict(S_NB, Soils[,6:14], type="class")
length(which(Gp_NB != Soils\$Gp))            ## 6 Errors
``````

This example tested on the training data, but illustrates that RF can make very good models.

``````    ######################################################
##    RF Regression Example
set.seed(656)            ## for reproducibility
S_RF_Reg = randomForest(pH ~ ., data=Soils[,6:14])
pH_RF = predict(S_RF_Reg, Soils[,6:14])

## Compare Predictions with Actual values for RF and Linear Model
S_LM = lm(pH ~ ., data=Soils[,6:14])
pH_LM = predict(S_LM, Soils[,6:14])
par(mfrow=c(1,2))
plot(Soils\$pH, pH_RF, pch=20, ylab="Predicted", main="Random Forest")
abline(0,1)
plot(Soils\$pH, pH_LM, pch=20, ylab="Predicted", main="Linear Model")
abline(0,1)
``````  PDF - Download R Language for free