title: sklearn and large datasets
body: <p>I have a dataset of 22 GB. I would like to process it on my laptop. Of course I can't load it in memory. </p>
  
  <p>I use a lot sklearn but for much smaller datasets. </p>
  
  <p>In this situations the classical approach should be something like. </p>
  
  <p>Read only part of the data -> Partial train your estimator -> delete the data -> read other part of the data -> continue to train your estimator.   </p>
  
  <p>I have seen that some sklearn algorithm have the partial fit method that should allow us to train the estimator with various subsamples of the data.</p>
  
  <p>Now I am wondering is there an easy why to do that in sklearn? 
  I am looking for something like </p>
  
  <pre><code>r = read_part_of_data('data.csv')
  m = sk.my_model
  `for i in range(n):
       x = r.read_next_chunk(20 lines)
       m.partial_fit(x)
  
  m.predict(new_x)
  </code></pre>
  
  <p>Maybe sklearn is not the right tool for these kind of things?
  Let me know.</p>