title: Specific Cross Validation with Random Forest
body: <p>Am using Random Forest with scikit learn.
  RF overfits the data and prediction results are bad.</p>
  
  <p>The overfit does NOT depend on the parameters of the RF:
     NBtree, Depth_Tree</p>
  
  <p>Overfit happens with many different parameters (Tested it across grid_search).</p>
  
  <p>To remedy:
      I tweak the initial data/ down sampling some results
      in order to affect the fitting (Manually pre-process noise sample).</p>
  
  <pre><code>Loop on random generation of RF fits, 
  
  Get RF prediction on the  data for prediction
  Select the model which best fits the "predicted data" (not the calibration data).
  </code></pre>
  
  <p>This Monte carlos is very consuming,
  Just wondering if there is another way to do 
  cross validation on random Forest ?  (ie NOT the hyper-parameter optimization).</p>
  
  <p>EDITED</p>