StatMixedML/Py-BoostLSS
An extension of Py-Boost to probabilistic modelling
This project helps data scientists and machine learning engineers create more robust predictions for scenarios with many interrelated outcomes. You feed in your dataset with multiple target variables, and it outputs probabilistic forecasts, including prediction intervals and quantiles, instead of just single point estimates. It's designed for users who need to understand the full range of potential outcomes and their likelihoods in complex, high-dimensional problems.
No commits in the last 6 months.
Use this if you need to predict multiple correlated outcomes simultaneously and want to understand the uncertainty and range of those predictions efficiently.
Not ideal if you are only predicting a single outcome or if your task doesn't require probabilistic forecasts with prediction intervals.
Stars
24
Forks
3
Language
Python
License
Apache-2.0
Category
Last pushed
Jan 19, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/StatMixedML/Py-BoostLSS"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
dmlc/xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python,...
catboost/catboost
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for...
stanfordmlgroup/ngboost
Natural Gradient Boosting for Probabilistic Prediction
lightgbm-org/LightGBM
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework...
fabsig/GPBoost
Tree-Boosting, Gaussian Processes, and Mixed-Effects Models