I'm studying the performance of an AdaBoost model and I wonder how it performs in regard to the depth of the trees.

Here's the accuracy for the model with a depth of 1


and here with a depth of 3


From my point of view, I would say the lower one looks better but somehow I guess the upper one is better as the training accuracy doesn't vanish (overfitting?)? The question resp. answer from Hyperparameter tunning for Random Forest- choose the best max depth underlines my assumption, though.

  • 2
    $\begingroup$ I think your y axis is labelled incorrectly. It should say log loss, not accuracy. $\endgroup$
    – Sammy
    Jul 19 at 5:00
  • $\begingroup$ true, I was also wondering.. but the question itself( would) remain(s)? $\endgroup$
    – Ben
    Jul 19 at 6:40

The training error shouldn't be too far from test error, otherwise it is a high deviance scenario and you could be in an overfitting situation in production.

However, having a higher deviance could be normal by increasing depth, but it shouldn't happen if you have enough data.

Consequently, if you haven't a lot of data, the depth of 1 seems better, and you should increase the training iterations to lower the error.

In addition to that, there is just a small difference in test results between the depth of 1 and the depth of 3. So, the small benefit of the depth of 3 doesn't worth the risk of having a high deviance scenario. But maybe max depth of 2 is better than 1...


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.