evaluate_model <- function()

in R/step3_training_evaluation.R [177:198]

• 22 lines of code
• 1 McCabe index (conditional complexity)

evaluate_model <- function(observed, predicted, model) {
  mean_observed <- mean(observed)
  se <- (observed - predicted)^2
  ae <- abs(observed - predicted)
  sem <- (observed - mean_observed)^2
  aem <- abs(observed - mean_observed)
  mae <- mean(ae)
  rmse <- sqrt(mean(se))
  rae <- sum(ae) / sum(aem)
  rse <- sum(se) / sum(sem)
  rsq <- 1 - rse
  metrics <- c("Mean Absolute Error" = mae,
               "Root Mean Squared Error" = rmse,
               "Relative Absolute Error" = rae,
               "Relative Squared Error" = rse,
               "Coefficient of Determination" = rsq)
  print(model)
  print(metrics)
  print("Summary statistics of the absolute error")
  print(summary(abs(observed-predicted)))
  return(metrics)
}