title: get Classification accuracy on test data using previous saved model
body: <p>I am using Orange data mining tool to write a python script to get classification accuracy on test data using a previous saved model(pickle file).</p>
  
  <pre><code>dataFile = "training.csv" 
  data = Orange.data.Table(dataFile);
  learner = Orange.classification.RandomForestLearner()
  cf = learner(data)
  #save the pickle file
  with open("1.pkcls", "wb") as f:
      pickle.dump(cf, f)
  
  #load the pickle file
  with open("1.pkcls", "rb") as f:
      loadCF = pickle.load(f)
  testFile = "testing.csv" 
  test = Orange.data.Table(testFile);
  
  learners = [1]
  learners[0] = cf
  result = Orange.evaluation.testing.TestOnTestData(data,test,learners)
  # get classification accuracy
  CAs = Orange.evaluation.CA(result)
  </code></pre>
  
  <p>I can successfully save and load the model but I had an error </p>
  
  <pre><code>    CAs = Orange.evaluation.CA(result)
  
  
  File "/Users/anaconda2/envs/py36/lib/python3.6/site-packages/Orange/evaluation/scoring.py", line 39, in __new__
      return self(results, **kwargs)
    File "/Users/anaconda2/envs/py36/lib/python3.6/site-packages/Orange/evaluation/scoring.py", line 48, in __call__
      return self.compute_score(results, **kwargs)
    File "/Users/anaconda2/envs/py36/lib/python3.6/site-packages/Orange/evaluation/scoring.py", line 84, in compute_score
      return self.from_predicted(results, skl_metrics.accuracy_score)
    File "/Users/anaconda2/envs/py36/lib/python3.6/site-packages/Orange/evaluation/scoring.py", line 75, in from_predicted
      dtype=np.float64, count=len(results.predicted))
    File "/Users/anaconda2/envs/py36/lib/python3.6/site-packages/Orange/evaluation/scoring.py", line 74, in &lt;genexpr&gt;
      for predicted in results.predicted),
    File "/Users/anaconda2/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py", line 172, in accuracy_score
      y_type, y_true, y_pred = _check_targets(y_true, y_pred)
    File "/Users/anaconda2/envs/py36/lib/python3.6/site-packages/sklearn/metrics/classification.py", line 82, in _check_targets
      "".format(type_true, type_pred))
  ValueError: Can't handle mix of multiclass and continuous
  </code></pre>
  
  <p>I find a way to fix this problem and successfully generate the classification accuracy by deleting</p>
  
  <pre><code>cf = learner(data)
  </code></pre>
  
  <p>However, if I delete this line of code, I am unable to train a model and save it because RandomForestLearner does not train the model based on the input file before code of saving and loading model.</p>
  
  <pre><code>with open("1.pkcls", "wb") as f:
  pickle.dump(cf, f)
  
  #load the pickle file
  with open("1.pkcls", "rb") as f:
  loadCF = pickle.load(f)
  </code></pre>
  
  <p>Does anyone know if it is possible to train a model first and save it as a pickle file. Then I can use it to test another file to get classification accuracy later?</p>