Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Aprendizaje activo× | LightGBM× | Random Forest× | |
|---|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning | Machine learning |
| Año de origen≠ | 2009 | 2017 | 2001 |
| Autor original≠ | Burr Settles | Ke, G. et al. (Microsoft) | Breiman, L. |
| Tipo≠ | Interactive supervised learning framework | Gradient boosting decision tree ensemble | Ensemble (bagging of decision trees) |
| Fuente seminal≠ | Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648. link ↗ | 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 ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Alias | Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Relacionados≠ | 2 | 5 | 4 |
| Resumen≠ | Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires. | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
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