Mondrian Forests for Large-Scale Regression when Uncertainty Matters
Many real-world regression problems demand a measure of the uncertainty associated with each prediction. Standard decision forests deliver efficient state-of-the-art predictive performance, but high-quality uncertainty estimates are lacking. Gaussian processes (GPs) deliver uncertainty estimates, bu...
| Asıl Yazarlar: | , , |
|---|---|
| Materyal Türü: | Conference item |
| Baskı/Yayın Bilgisi: |
19th International Conference on Artificial Intelligence and Statistics
2016
|