1
$\begingroup$

I want to train a binary classification model using some tree ensemble (either xgboost or random forests). My dataset has some 50 features, and I believe some of them are redundant (there's correlation between some features, among other things).

I am wondering what order should I perform the following two tasks

  • Optimize hyperparameters for the tree ensemble (e.g. number of trees, tree depth etc.) via $k$-fold cross validation

  • Recursive Feature Elimination: Fix hyperparameters and train a model. Remove features one at a time (after each model is trained) based on feature weights (lower weights => less importance). That is, train a model on $p$ features, remove feature $j$ with the lowest weight so now we have $p-1$ features, and repeat this until either i) the desired number of features are left or ii) the accuracy hits a lower threshold

What I am concerned about is if I remove features first and then optimize hyperparameters second, I may be loosing out on some complex feature + hyperparameter interactions. However if I do the converse, tune hyperparameters with all features, then remove features, I may end up with a dataset for which the hyperparameters we found are no longer optimal.

Is there a correct way / order to do this?

$\endgroup$
1
$\begingroup$

For tree based model, it can automatically handle redundant features, i.e. less useful features will not be selected as a split point. So you do not need to manually handle feature selection problems.

In many implementations of random forest or tree based boosting, the algorithm will automatically select a subset of features to build each tree. Therefore, from computational complexity perspective, thousands features are fine (In your case you only have 50 features.)

$\endgroup$
1
$\begingroup$

Is there a correct way / order to do [two kind of hyperparameter optimization]?

Yes: unless you know for sure that the different hyperparameters do not interact, they should to be optimized together.

Here, they do interact => optimize together

You can also optimize sequentially, but that should then become an iterative procedure:

  1. optimize one type of hyperparameter
  2. optimize the other
  3. repeat until the two optimization steps do not yield changes any more.

And of course, that optimum may be only a local optimum.


Also, please read up on randomForest. A large part of your optimization is done already by rF, and some features (most prominently no. of trees) of rF behave differently wrt. optimization from what you may be thinking.

$\endgroup$

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.