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| 설명 가능한 K-최근접 이웃 (Explainable K-Nearest Neighbors, XKNN)× | 나이브 베이즈× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1967 (KNN); 2010s (explainability extensions) | 1997 |
| 창시자≠ | Cover, T. & Hart, P. (KNN); XAI extensions by various authors | Mitchell, T. M. (textbook treatment) |
| 유형≠ | Instance-based learning with explainability layer | Probabilistic classifier (Bayes' theorem with conditional independence) |
| 원전≠ | Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI ↗ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 |
| 별칭≠ | XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes |
| 관련 | 4 | 4 |
| 요약≠ | Explainable K-Nearest Neighbors (XKNN) augments the classic KNN classifier or regressor with structured post-hoc or built-in explanation mechanisms, exposing which retrieved neighbors, which features, and which distance contributions drive each individual prediction — making the model's reasoning transparent and auditable for human decision-makers. | Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate. |
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