[Project] PerpetualBooster v1.9.4 - a GBM that skips the hyperparameter tuning step entirely. Now with drift detection, prediction intervals, and causal inference built in.

Summary

Hey r/datascience , If you've ever spent an afternoon watching Optuna churn through 100 LightGBM trials only to realize you need to re-run everything after fixing a feature, this is the tool I wish I had. Perpetual is a gradient boosting machine (Rust core, Python/R bindings) that replaces hyperparameter tuning with a single budget parameter. You set it, train once, and the model generalizes itself internally. No grid search, no early stopping tuning, no validation set ceremony. ```python fro...