Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Isolation Forest× | Árvore de Decisão× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 2008 | 1984 |
| Autor original≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | Breiman, Friedman, Olshen & Stone |
| Tipo≠ | Unsupervised ensemble (random partitioning trees) | Recursive partitioning (if-then rules) |
| Fonte seminal≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Outros nomes≠ | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Relacionados | 5 | 5 |
| Resumo≠ | 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. | 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. |
| ScholarGateConjunto de dados ↗ |
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