title: Python Scikit Random Forest Regressor Error
body: <p>I am trying to load training and test data from a csv, run the random forest regressor in scikit/sklearn, and then predict the output from the test file.</p>
  
  <p>The TrainLoanData.csv file contains 5 columns; the first column is the output and the next 4 columns are the features. The TestLoanData.csv contains 4 columns - the features.</p>
  
  <p>When I run the code, I get error: </p>
  
  <pre><code>    predicted_probs = ["%f" % x[1] for x in predicted_probs]
  IndexError: invalid index to scalar variable.
  </code></pre>
  
  <p>What does this mean?</p>
  
  <p>Here is my code: </p>
  
  <pre><code>import numpy, scipy, sklearn, csv_io //csv_io from https://raw.github.com/benhamner/BioResponse/master/Benchmarks/csv_io.py
  from sklearn import datasets
  from sklearn.ensemble import RandomForestRegressor
  
  def main():
      #read in the training file
      train = csv_io.read_data("TrainLoanData.csv")
      #set the training responses
      target = [x[0] for x in train]
      #set the training features
      train = [x[1:] for x in train]
      #read in the test file
      realtest = csv_io.read_data("TestLoanData.csv")
  
      # random forest code
      rf = RandomForestRegressor(n_estimators=10, min_samples_split=2, n_jobs=-1)
      # fit the training data
      print('fitting the model')
      rf.fit(train, target)
      # run model against test data
      predicted_probs = rf.predict(realtest)
      print predicted_probs
      predicted_probs = ["%f" % x[1] for x in predicted_probs]
      csv_io.write_delimited_file("random_forest_solution.csv", predicted_probs)
  
  main()
  </code></pre>