pipeline R

autoDtree <- function(tree) {
  #user input
  ms <- readline(prompt="minsplit: ")
  c <- readline(prompt="C-value: ")
  md <- readline(prompt="maxdepth: ")
  #transform
  ms <- as.integer(ms)
  c <- as.numeric(c)
  md <- as.integer(md)
  #decision tree
  tree <- rpart(form, TREINO, control = rpart.control(minsplit = (ms),  cp = (c), maxdepth = (md)))
  ptree <- predict(tree, VALID)
  #ROC(AUC)
  roctree <- roc.curve(scores.class0 = ptree, weights.class0 = label_valid_X[,label], curve=TRUE)
  #Confusion Matrix
  rptree <- round(ptree)
  cm <- confusionMatrix(as.factor(rptree), as.factor(VALID[,'comprou_X']))

  #METRICS (precision e recall)
    metrics <- function(cm) {

      tp <- cm['table']$table[4]
      tn <- cm['table']$table[1]
      fp <- cm['table']$table[2]
      fn <- cm['table']$table[3]

      precision <- tp / (tp + fp)
      recall <- tp / (tp + fn)

      values <- c(precision, recall)

      return(values)

    }
  #print metrics
  metrics(cm)

  #print decision tree
  par(mar = rep(2, 4))
  tplot <- rpart.plot(tree)


  par(mar = rep(2, 4))
  troc <- plot(roctree)


}


autoDtree(tree)