方法对比
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| K-Nearest Neighbors× | 决策树× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1967 | 1984 |
| 提出者≠ | Cover, T.M. & Hart, P.E. | Breiman, Friedman, Olshen & Stone |
| 类型≠ | Instance-based (non-parametric) learning | Recursive partitioning (if-then rules) |
| 开创性文献≠ | Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| 别名≠ | KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| 相关 | 5 | 5 |
| 摘要≠ | K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values. | 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. |
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