Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| LightGBM× | Isolation Forest× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2017 | 2008 |
| Autors≠ | Ke, G. et al. (Microsoft) | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tips≠ | Gradient boosting decision tree ensemble | Unsupervised ensemble (random partitioning trees) |
| Pirmavots≠ | 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 ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Citi nosaukumi≠ | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets. |
| ScholarGateDatu kopa ↗ |
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