Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Neirālā ODE× | Random Forest× | XGBoost× | |
|---|---|---|---|
| Nozare≠ | Dziļā mācīšanās | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2018 | 2001 | 2016 |
| Autors≠ | Chen, T. Q. et al. | Breiman, L. | Chen, T. & Guestrin, C. |
| Tips≠ | Continuous-depth neural network (ODE-parameterised dynamics) | Ensemble (bagging of decision trees) | Ensemble (gradient-boosted decision trees) |
| Pirmavots≠ | Chen, T. Q., Rubanova, Y., Bettencourt, J. & Duvenaud, D. (2018). Neural Ordinary Differential Equations. Advances in Neural Information Processing Systems (NeurIPS). link ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Citi nosaukumi≠ | Nöral Diferansiyel Denklem (Neural ODE), neural ordinary differential equation, continuous-depth network, ODE-Net | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble | XGBoost, extreme gradient boosting, scalable tree boosting |
| Saistītās≠ | 4 | 4 | 5 |
| Kopsavilkums≠ | A Neural ODE, introduced by Chen and colleagues in 2018, models a hidden state as the continuous solution of an ordinary differential equation whose dynamics are parameterised by a neural network. It generalises the limiting case of residual connections, making it well suited to irregularly spaced time series and physics-based modelling. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. |
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