title: Residual Estimator
body: <p>I have such a pipline:</p>
  
  <pre><code>attribute_est = Pipeline([
       ('jsdf', DictVectorizer()),
       ('clf', Ridge())
      ])
  </code></pre>
  
  <p>In there I pass data like:</p>
  
  <pre><code>{
    'Master_card' : 1,
    'Credit_Cards': 1,
    'casual_ambiance': 0,
    'Classy_People': 0
  }
  </code></pre>
  
  <p>My model does not predict that well. Now I got proposed to:</p>
  
  <blockquote>
    <p>You may find it difficult to find a single regressor that does well
    enough. A common solution is to use a linear model to fit the linear
    part of some data, and use a non-linear model to fit the residual that
    the linear model can't fit. Build a residual estimator that takes as
    an argument two other estimators. It should use the first to fit the
    raw data and the second to fit the residuals of the first.</p>
  </blockquote>
  
  <p><strong>What is meant with a Residual estimator? Can you provide me with an example please?</strong></p>