For Machine Learning and Web Analytics blogpost http://markedmondson.me/intro-to-machine-learning-with-web-analytics-random-forests-and-k-means

ml_model_assess_randomForest.R

library(randomForest)
## warning - can take a long time (30mins)
rf <- randomForest(x = predictors, y = response)

## once model done, we run it using test data and compare results to reality
predictor_test <- test[,which(!names(test) %in% c("dimension1","boughtSku"))]
response_test <- as.factor(test[,"boughtSku"])

## check result on test set
prediction <- predict(rf, predictor_test)
predictor_test$correct <- as.character(prediction) == as.character(response_test)

## How many were correct?
table(as.character(prediction) == as.character(response_test)) 
accuracy <- sum(predictor_test$correct) / nrow(predictor_test)