Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

multiplicative quantile regression mode #35

Merged
merged 4 commits into from
Aug 16, 2023
Merged

Conversation

FelixWick
Copy link
Collaborator

Implementation of quantile regression using a numerical minimization (via scipy) of a quantile loss.
solving #2

For now, this is done for multiplicative mode, i.e., non-negative target values, only (simply via a multiplicative model used in the loss function), but a generalization to additive mode is trivial. However, I would suggest to do such a generalization in a separate pull request (for #32 ) introducing a generic mode (using the structure implemented in this pull request) allowing for numerical minimization of arbitrary losses (i.e., also allowing likelihoods for example).

@FelixWick FelixWick linked an issue Aug 14, 2023 that may be closed by this pull request
cyclic_boosting/regression.py Show resolved Hide resolved
tests/test_integration.py Show resolved Hide resolved
@FelixWick FelixWick merged commit 2a8f4c6 into main Aug 16, 2023
8 checks passed
@FelixWick FelixWick deleted the quantile_regression branch August 16, 2023 07:32
setoguchi-naoki added a commit to PanasonicConnect/cyclic-boosting that referenced this pull request Mar 28, 2024
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

add quantile regression mode
3 participants