title: Memory efficient classifiers in R for extremely wide and not too long training set
body: <p>Training data set is is extremely wide (about 200K features) and very short (in hundreds). Obviously the data set occupies a lot of memory but R reads it without problems. </p>
  
  <p>Then I trained Random Forest classifier and it ran out of memory on it. So I am switching to simpler classifier like Naive Bayes. NB resulted in out of memory too.</p>
  
  <p>Generally, what are most memory efficient classifiers? I suspect that logistic regression and Naive Bayes should make the list...</p>
  
  <p><strong>UPD:</strong></p>
  
  <p>I ended up using feature reduction methods before using random forest. Package <a href="http://caret.r-forge.r-project.org/index.html" rel="nofollow">caret</a> can help but not with initial number of variables in my case.</p>
  
  <p>Feature reductions used:</p>
  
  <ul>
  <li>variance threshold filter (removed features with variance below threshold);</li>
  <li>correlation between feature and predicted values: removed features with low correlation;</li>
  <li>feature pair-wise correlations: removing high pair-wise correlation features.</li>
  </ul>