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Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| LightGBM× | Isolation Forest× | |
|---|---|---|
| Odbor | Strojové učenie | Strojové učenie |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2017 | 2008 |
| Tvorca≠ | Ke, G. et al. (Microsoft) | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Typ≠ | Gradient boosting decision tree ensemble | Unsupervised ensemble (random partitioning trees) |
| Pôvodný zdroj≠ | 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 ↗ |
| Ďalšie názvy≠ | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Príbuzné | 5 | 5 |
| Zhrnutie≠ | 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. |
| ScholarGateDátová sada ↗ |
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