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A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.

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Hello! When I assess my CPU usage using `top`, I see only 1 CPU being used. Is it possible to enable or implement multiprocessing?

Thanks for this repo! I wonder if it is possible to extend the `BoostingRegressor` to work with the data where censoring points `yl`, `yu` are varied by observations.

Hi Mr. Sigrist: Thank you for implementing such a cool algorithm! I am wondering whether it is possible to add a monotone constraint to the main function. This is crucial...