方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 决策树× | 孤立森林 (Isolation Forest)× | 随机森林× | |
|---|---|---|---|
| 领域 | 机器学习 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning | Machine learning |
| 起源年份≠ | 1984 | 2008 | 2001 |
| 提出者≠ | Breiman, Friedman, Olshen & Stone | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | Breiman, L. |
| 类型≠ | Recursive partitioning (if-then rules) | Unsupervised ensemble (random partitioning trees) | Ensemble (bagging of decision trees) |
| 开创性文献≠ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| 别名≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| 相关≠ | 5 | 5 | 4 |
| 摘要≠ | 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. | 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. | 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. |
| ScholarGate数据集 ↗ |
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