R/print.randomForest.R

In karlkumbier/iRF2.0: Iterative Random Forests

#' Print random forest
#' @method print randomForest
#' @export
"print.randomForest" <-
function(x, ...) {
  cat("\nCall:\n", deparse(x$call), "\n")
  cat("               Type of random forest: ", x$type, "\n", sep="")
  cat("                     Number of trees: ", x$ntree, "\n",sep="")
  cat("No. of variables tried at each split: ", x$mtry, "\n\n", sep="")
  if(x$type == "classification") {
    if(!is.null(x$confusion)) {
      cat("        OOB estimate of  error rate: ",
          round(x$err.rate[x$ntree, "OOB"]*100, digits=2), "%\n", sep="")
      cat("Confusion matrix:\n")
      print(x$confusion)
      if(!is.null(x$test$err.rate)) {
        cat("                Test set error rate: ",
            round(x$test$err.rate[x$ntree, "Test"]*100, digits=2), "%\n",
            sep="")
        cat("Confusion matrix:\n")
        print(x$test$confusion)
      }
    }
  }
  if(x$type == "regression") {
    if(!is.null(x$mse)) {
      cat("          Mean of squared residuals: ", x$mse[length(x$mse)],
          "\n", sep="")
      cat("                    % Var explained: ",
          round(100*x$rsq[length(x$rsq)], digits=2), "\n", sep="")
      if(!is.null(x$test$mse)) {
        cat("                       Test set MSE: ",
            round(x$test$mse[length(x$test$mse)], digits=2), "\n", sep="")
        cat("                    % Var explained: ",
            round(100*x$test$rsq[length(x$test$rsq)], digits=2), "\n", sep="")
      }      
    }
    if (!is.null(x$coefs)) {
      cat("  Bias correction applied:\n")
      cat("  Intercept: ", x$coefs[1], "\n")
      cat("      Slope: ", x$coefs[2], "\n")
    }
  }
}