title: How to make predictions using a saved RandomForest model from Orange GUI
body: <p>I used Orange GUI and trained a RandomForest model that i later saved using the SaveModel widget.</p>
  
  <p>Orange saves its models with pickle, therefore I went and wrote the following python script:</p>
  
  <pre><code>import Orange, pickle
  
  model = pickle.load(open('model', 'rb'))
  </code></pre>
  
  <p>The problem is, I extensively searched the web yesterday. And couldn't find any example to make a prediction with my data (which is the same format as the data I used in the Orange GUI) or sufficient documentation on how to use the model.</p>
  
  <p>Upon later research I found that I could supposedly evaluate a pre-trained model that was unpickled with the following code</p>
  
  <pre><code>results = Orange.evaluation.testing.TestOnTestData(data, test, [lambda testdata: model])
  </code></pre>
  
  <p>The thing is to load data I'm supposed to do:</p>
  
  <pre><code>data = Orange.data.Table('trainingData.csv');
  test = Orange.data.Table('testData.csv');
  </code></pre>
  
  <p>And I haven't been able to find documentation regarding how to differentiate between the target and the features in these <code>*.csv</code> files.</p>
  
  <p>Appart from that even if I'm able create this files. I would have to do some gimmick where <code>testData.csv</code> would need to be only one line long (the line I want to predict) with a target value of 1. And I would see what the model predicted by checking if the score was <code>100%</code> or <code>0%</code></p>
  
  <p>So I know my questions are multiple but I could really use some help on the follwing points:</p>
  
  <ol>
  <li>How to define in a <code>*.csv</code> file what is the target among the features for the <code>Orange.data.Table()</code> function</li>
  <li>How to use a pickled Orange RandomForest model to make a prediction instead of using a gimmicked evaluation to make a prediction. (So that i could predict more than one item at a time..)</li>
  </ol>
  
  <p>Thank you very much for your time</p>