title: How to choose the features that describe x% of all information in data while using Incremental principal components analysis (IPCA)?
body: <p>I'd like to use the <a href="http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.IncrementalPCA.html#sklearn.decomposition.IncrementalPCA" rel="nofollow noreferrer">Incremental principal components analysis</a> (IPCA) to reduce my feature space such that it contains x% of information.</p>
  
  <p>I would use the <code>sklearn.decomposition.IncrementalPCA(n_components=None, whiten=False, copy=True, batch_size=None)</code>
  I can leave the <code>n_components=None</code> so that it works on all the features that I have.</p>
  
  <p>But later once the whole data set is analyzed. 
  How do I select the features that represent x% of data and how do I create a <code>transform()</code> for those features number of features.</p>
  
  <p>This idea taken from this <a href="https://datascience.stackexchange.com/questions/10730/how-many-dimensions-to-reduce-to-when-doing-pca">question.</a></p>