Specific Cross Validation with Random Forest
<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>