Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Päätöspuu× | LightGBM× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 1984 | 2017 |
| Kehittäjä≠ | Breiman, Friedman, Olshen & Stone | Ke, G. et al. (Microsoft) |
| Tyyppi≠ | Recursive partitioning (if-then rules) | Gradient boosting decision tree ensemble |
| Alkuperäislähde≠ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems (NeurIPS) 30, 3146–3154. link ↗ |
| Rinnakkaisnimet≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf. | LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy. |
| ScholarGateAineisto ↗ |
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