How to tune parameters in Random Forest, using Scikit Learn?

Question

class sklearn.ensemble.RandomForestClassifier(n_estimators=10,
                                              criterion=\'gini\', 
                                            


        

Answer 1

The crucial parts are usually three elements:

• number of estimators - usually bigger the forest the better, there is small chance of overfitting here
• max depth of each tree (default none, leading to full tree) - reduction of the maximum depth helps fighting with overfitting
• max features per split (default sqrt(d)) - you might one to play around a bit as it significantly alters behaviour of the whole tree. sqrt heuristic is usually a good starting point but an actual sweet spot might be somewhere else