title: Train random forest with caret using parallel
body: <p>I would like to make use of the 20 CPU cores I have at hand to train random forests in R. My usual code using randomForest package would be this:</p>
  
  <pre><code>rf = randomForest(Pred~., train, ntree=100, importance=TRUE)
  rf
  </code></pre>
  
  <p>So I train a forest with 100 trees using a factor <code>Pred</code> with 11 levels and a dataframe <code>train</code> with 74 numeric features and ~84k observations.</p>
  
  <p>The idea was to speed this up by using caret with my code (derived from this <a href="https://github.com/lgreski/datasciencectacontent/blob/master/markdown/pml-randomForestPerformance.md" rel="nofollow noreferrer">example</a>):</p>
  
  <pre><code>cluster &lt;- makeCluster(19)
  registerDoParallel(cluster)
  trainctrl &lt;- trainControl(method="none", number=1, allowParallel=TRUE)
  fit &lt;- train(Driver~., train, method="parRF", trControl=trainctrl, ntree=100)
  stopCluster(cluster)
  registerDoSEQ()
  fit
  </code></pre>
  
  <p>I replaced <code>method=cv</code> from the example with <code>method=none</code> as I do want to train on the whole training set (see <a href="https://www.rdocumentation.org/packages/caret/versions/6.0-81/topics/trainControl" rel="nofollow noreferrer">documentation</a>). However I do not get an accuracy from <code>fit</code>, <code>fit$results</code> is empty. If I set <code>method=oob</code> an optimization of <code>mtry</code> is done, which also gives me accuracies.</p>
  
  <p><strong>Is there a way to simply run the first code snippet in parallel using caret without any hyperparameter optimizations?</strong></p>