In the previous section, we learned how Random Forest builds multiple trees to make predictions. Increasing the number of trees does improve model performance but it usually doesn't help much to decrease the risk of overfitting. Our model in the previous example is still performing much better on the training set (data it has already seen) than on the testing set (unseen data).
So, we are not confident enough yet to say the model will perform well in production. There are different hyperparameters that can help to lower the risk of overfitting for Random Forest and one of them is called
This hyperparameter defines the depth of the trees built by Random Forest. Basically, it tells Random Forest model, how many nodes (questions) it can create before making predictions. But how will that help to reduce overfitting, you may ask. Well, let's say you built a single tree and set the
max_depth hyperparameter to
50. This would mean that there would...