accuracy_calc_function.R

#### Function to validate classification models####

accuracy_metrics <- function(y_actual, y_predicted) {
  
  #Check if the input is factor
  
  if(is.factor(y_actual)){
    print("Info : Actual values provided as factor. Factor converted to numeric. ")
    #Convert to numeric
    y_actual <- as.numeric(y_actual) - 1
  }
  
  if(is.factor(y_predicted)){
    print("Info : Predicted values provided as factor. Factor converted to numeric. ")
    #Convert to numeric
    y_predicted <- as.numeric(y_predicted) - 1 
  }
  
  if(is.logical(y_actual)){
    print("Info : Predicted values provided as Logical vector. Logical vector converted to numeric. ")
    #Convert to numeric
    y_actual<- as.numeric(y_actual)
  }
  
  if(is.logical(y_predicted)){
    print("Info : Predicted values provided as Logical vector. Logical vector converted to numeric. ")
    #Convert to numeric
    y_predicted<- as.numeric(y_predicted)
  }
  
  
  # AUC ROC
  pred_ROCR <- prediction(y_predicted, y_actual)
  roc_ROCR <- performance(pred_ROCR, measure = "tpr", x.measure = "fpr")
  plot(roc_ROCR, main = "ROC curve", colorize = T)
  abline(a = 0, b = 1)
  
  auc_ROCR <- performance(pred_ROCR, measure = "auc")
  auc_ROCR <- auc_ROCR@y.values[[1]]

  print(paste("Area under ROC = ", auc_ROCR))
  return(auc_ROCR)
}