title: Is there no "inverse_transform" method for a scaler like MinMaxScaler in spark?
body: <p>When train a model, say linear regression, we may make a normalization, like MinMaxScaler, on the train an test dataset.</p>
  
  <p>After we got a trained model and use it to make predictions, and scale back the predictions to the original representation.</p>
  
  <p>In python, there is "inverse_transform" method. For example:</p>
  
  <pre><code>from sklearn.preprocessing import MinMaxScaler
  scalerModel.inverse_transform
  
  from sklearn.preprocessing import MinMaxScaler
  
  data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
  
  scaler = MinMaxScaler()
  MinMaxScaler(copy=True, feature_range=(0, 1))
  print(data)
  
  dataScaled = scaler.fit(data).transform(data)
  print(dataScaled)
  
  scaler.inverse_transform(dataScaled)
  </code></pre>
  
  <p>Is there similar method in spark?</p>
  
  <p>I have googled a lot, but found no answer. Can anyone give me some suggestions? 
  Thank you very much!</p>