title: How to tune parameters in Random Forest, using Scikit Learn?
body: <pre><code>class sklearn.ensemble.RandomForestClassifier(n_estimators=10,
                                                criterion='gini', 
                                                max_depth=None,
                                                min_samples_split=2,
                                                min_samples_leaf=1, 
                                                min_weight_fraction_leaf=0.0, 
                                                max_features='auto', 
                                                max_leaf_nodes=None, 
                                                bootstrap=True, 
                                                oob_score=False,
                                                n_jobs=1, 
                                                random_state=None,
                                                verbose=0, 
                                                warm_start=False, 
                                                class_weight=None)
  </code></pre>
  
  <p>I'm using a random forest model with 9 samples and about 7000 attributes.  Of these samples, there are 3 categories that my classifier recognizes. </p>
  
  <p>I know this is far from ideal conditions but I'm trying to figure out which attributes are the most important in feature predictions.  Which parameters would be the best to tweak for optimizing feature importance? </p>
  
  <p>I tried different <code>n_estimators</code> and noticed that the amount of "significant features" (i.e. nonzero values in the <code>feature_importances_</code> array) increased dramatically. </p>
  
  <p>I've read through the documentation but if anyone has any experience in this, I would like to know which parameters are the best to tune and a brief explanation why. </p>