I would like to create a random forest using the following process:
- Build a tree on a random samples of the data and features using information gain to determine splits
- Terminate a leaf node if it exceeds a pre-defined depth OR any split would result in a leaf count less than a pre-defined minimum
- Rather than assign a class label for each tree, assign the proportion of classes in the leaf node
- Stop building trees after a pre-defined number have been constructed
This bucks the traditional random forest process in two ways. One, it uses pruned trees that assign proportions rather than class labels. And two, the stop criteria is a pre-determined number of trees rather than some out-of-bag error estimate.
My question is this:
For the above process that outputs N trees, can I then fit a model using logistic regression with LASSO selection? Does anyone have experience fitting a Random Forest classifier and post-processing with logistic LASSO?
The ISLE framework mentions using LASSO as a post-processing step for regression problems but not classification problems. Furthermore, I don't get any helpful results when googling "Random forest lasso".