title: How to inverse transform regression predictions after pipeline?
body: <p>I'm trying to figure out how to unscale my data (presumably using inverse_transform) for predictions when I'm using a pipeline. The data below is just an example. My actual data is much larger and complicated, but I'm looking to use RobustScaler (as my data has outliers) and Lasso (as my data has dozens of useless features). I am new to pipelines in general. </p>
  
  <p>Basically, if I try to use this model to predict anything, I want that prediction in unscaled terms. Is this possible with a pipeline? How can I do this with inverse_transform? </p>
  
  <pre><code>import pandas as pd
  from sklearn.linear_model import Lasso
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import RobustScaler
  
  data = [[100, 1, 50],[500 , 3, 25],[1000 , 10, 100]]
  df = pd.DataFrame(data,columns=['Cost','People', 'Supplies'])
  
  X = df[['People', 'Supplies']]
  y = df[['Cost']]
  
  #Split
  X_train,X_test,y_train,y_test = train_test_split(X,y)
  
  #Pipeline
  pipeline = Pipeline([('scale', RobustScaler()),
              ('alg', Lasso())])
  
  clf = pipeline.fit(X_train,y_train)
  
  train_score = clf.score(X_train,y_train)
  test_score = clf.score(X_test,y_test)
  
  print ("training score:", train_score)
  print ("test score:", test_score)
  
  #Predict example 
  example = [[10,100]]
  clf.predict(example)
  </code></pre>