Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Monikerki-kerrosperceptron (MLP)× | Random Forest× | XGBoost× | |
|---|---|---|---|
| Tieteenala≠ | Syväoppiminen | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning | Machine learning |
| Syntyvuosi≠ | 1986 | 2001 | 2016 |
| Kehittäjä≠ | Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. | Breiman, L. | Chen, T. & Guestrin, C. |
| Tyyppi≠ | Supervised feedforward neural network | Ensemble (bagging of decision trees) | Ensemble (gradient-boosted decision trees) |
| Alkuperäislähde≠ | Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536. DOI ↗ | 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 ↗ |
| Rinnakkaisnimet≠ | MLP, feedforward neural network, fully connected neural network, vanilla neural network | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble | XGBoost, extreme gradient boosting, scalable tree boosting |
| Liittyvät≠ | 4 | 4 | 5 |
| Tiivistelmä≠ | A Multilayer Perceptron is a classic fully connected feedforward neural network trained with the backpropagation algorithm, as formalised by Rumelhart, Hinton & Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons, and an output layer, the MLP learns nonlinear mappings from input features to target outputs and serves as the foundational building block of modern deep learning. | 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. |
| ScholarGateAineisto ↗ |
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