Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| LightGBM× | Isolation Forest× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 2017 | 2008 |
| Autor original≠ | Ke, G. et al. (Microsoft) | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tipo≠ | Gradient boosting decision tree ensemble | Unsupervised ensemble (random partitioning trees) |
| Fonte seminal≠ | 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 ↗ |
| Outros nomes≠ | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Relacionados | 5 | 5 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
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