Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Bayesian Random Forest× | Boosting× | Semi-supervised Boosting× | |
|---|---|---|---|
| Fagfelt | Maskinlæring | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning | Machine learning |
| Opprinnelsesår≠ | 2015 | 1990–1997 | 1999–2009 |
| Opphavsperson≠ | Taddy, M. et al. | Schapire, R. E.; Freund, Y. | Mallapragada, P. K.; Bennett, K. P.; and others |
| Type≠ | Bayesian ensemble of decision trees | Sequential ensemble (iterative reweighting) | Semi-supervised ensemble method |
| Opprinnelig kilde≠ | Taddy, M., Chen, C., Yu, J., & Wyle, M. (2015). Bayesian and Empirical Bayesian Forests. Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), PMLR 37, 967–976. link ↗ | Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. DOI ↗ | Mallapragada, P. K., Jin, R., Jain, A. K., & Liu, Y. (2009). SemiBoost: Boosting for Semi-supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 2000–2014. DOI ↗ |
| Alias | Bayesian Forest, BRF, Empirical Bayesian Forest, posterior random forest | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting |
| Relaterte≠ | 5 | 6 | 5 |
| Sammendrag≠ | Bayesian Random Forest extends the classical random forest by placing a prior distribution over tree structures and leaf parameters, then sampling or approximating the posterior over that ensemble. The result is a set of predictions accompanied by calibrated uncertainty estimates — a capability standard random forests lack — making it valuable when knowing how confident the model is matters as much as the prediction itself. | Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy. | Semi-supervised Boosting is an ensemble learning paradigm that extends classical boosting algorithms — such as AdaBoost — to exploit both labeled and unlabeled data. By propagating label information through a similarity structure over unlabeled instances, it trains stronger classifiers than supervised boosting alone when labeled data are scarce. |
| ScholarGateDatasett ↗ |
|
|
|