We can clearly see that the Random Forest model is overfitting when the parameter value is very low (when parameter value < 100), but the model performance quickly rises up and rectifies the issue of overfitting (100 < parameter value < 400).
Does overfitting happen in random forest?
Random Forests do not overfit. The testing performance of Random Forests does not decrease (due to overfitting) as the number of trees increases. Hence after certain number of trees the performance tend to stay in a certain value.
How do you control overfitting in random forest?
1 Answer
- n_estimators: The more trees, the less likely the algorithm is to overfit. ...
- max_features: You should try reducing this number. ...
- max_depth: This parameter will reduce the complexity of the learned models, lowering over fitting risk.
- min_samples_leaf: Try setting these values greater than one.
