step3_predicting_linear_model_by_karanshah

step3.R

act_pred <- data.frame(cbind(actuals=testData$HEMATOCRIT, predicteds=predict)) # actuals_predicteds 
cor(act_pred) # correlation_accuracy
head(act_pred, n=10)

# Actual values and predicted ones seem very close to each other. A good metric to see how much they are close is the min-max accuracy, that considers the average between the minimum and the maximum prediction.

min_max <- mean(apply(act_pred, 1, min) / apply(act_pred, 1, max))  
print(min_max) # show the result

mape <- mean(abs((act_pred$predicteds - act_pred$actuals))/act_pred$actuals)
print(mape) # show the result