title: MLR and randomForestSRC: Incoherence between computers
body: <p>Our team run the following code to create a random forest model and train it:</p>
  
  <pre><code># Define a cross validation strategy
  rdesc &lt;- makeResampleDesc("CV", iters = cv_fold, predict = "both")
  
  # Define a (regression) task
  task_01 = makeRegrTask(data = data.model, target = "target_actual")
  
  # Make a learner
  lrn_rf = makeLearner("regr.randomForestSRC", predict.type = "response",
                       fix.factors.prediction = TRUE,
                       par.vals = list(nodesize = 50, mtry = 36, ntree = 500))
  
  set.seed(7)
  model_rf = mlr::resample(lrn_rf, task_01, rdesc, models = TRUE, 
                           extract = function(x) getLearnerModel(x),
                           measures = list(rmse, rsq), show.info = FALSE)
  
  model_rf
  </code></pre>
  
  <p>Mostly, the model predicts coherent results that make sense. However, when I run exactly the same code (no changes whatsoever) on the computers of two of my colleagues, the model predicts those weird results:</p>
  
  <pre><code>Resample Result
  Task: data.model
  Learner: regr.randomForestSRC
  Aggr perf: rmse.test.rmse=361.1464455,rsq.test.mean=-588.1729057
  Runtime: 4.0032
  </code></pre>
  
  <p>What could be the reason for this weird behavior on only two computers but not the other ones?</p>