Min_Max Accuracy => mean(min(actual, predicted)/max(actual, predicted))

min_max_accuracy <- mean(apply(actuals_preds, 1, min) / apply(actuals_preds, 1, max))

set.seed(100)  # setting seed to reproduce results of random sampling
trainingRowIndex <- sample(1:nrow(cars), 0.8*nrow(cars))  # row indices for training data
trainingData <- cars[trainingRowIndex, ]  # model training data
testData  <- cars[-trainingRowIndex, ]
lmMod <- lm(dist ~ speed, data=trainingData)  # build the model
distPred <- predict(lmMod, testData)  # predict distance
actuals_preds <- data.frame(cbind(actuals=testData$dist, predicteds=distPred))

apply(actuals_preds, 1, min)

apply(actuals_preds, 1, max)

mean(min(actual, predicted)/max(actual, predicted))