ngboost
natural-posterior-network
| ngboost | natural-posterior-network | |
|---|---|---|
| 1 | 1 | |
| 1,587 | 71 | |
| 1.0% | - | |
| 6.7 | 0.0 | |
| 3 months ago | about 1 year ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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ngboost
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Exploring Estimations and Confidence Intervals of Three Point Shooting
For DARKO, I do this by passing my time-decay/padding/kalman calculations as features (along with other pieces of biographical information) into NGboost. This gives me a point-estimate prediction, as well as confidence intervals. NGboost is somewhat limited in terms of what distributions it can handle, so you need to use care here, but it's very powerful since it can easily work with a large feature space (DARKO uses hundreds of features).
natural-posterior-network
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[R] PyTorch Implementation of the Natural Posterior Network
Therefore, we put serious effort in the publicly available implementation to facilitate usage of NatPN: we (1) provide an intuitive interface that enables using the model as easily as Scikit-learn estimators and (2) follow a modular design that allows you to customize and build upon the model at different levels of abstraction. Check it out on GitHub!
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