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| Cây Quyết định× | LIME: Giải thích Mô hình Cục bộ Có thể Diễn giải và Độc lập Mô hình× | Naive Bayes× | |
|---|---|---|---|
| Lĩnh vực | Học máy | Học máy | Học máy |
| Họ | Machine learning | Machine learning | Machine learning |
| Năm ra đời≠ | 1984 | 2016 | 1997 |
| Người khởi xướng≠ | Breiman, Friedman, Olshen & Stone | Marco Ribeiro, Sameer Singh & Carlos Guestrin | Mitchell, T. M. (textbook treatment) |
| Loại≠ | Recursive partitioning (if-then rules) | post-hoc local explanation | Probabilistic classifier (Bayes' theorem with conditional independence) |
| Công trình gốc≠ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. ACM SIGKDD, 1135–1144. DOI ↗ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 |
| Tên gọi khác≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Local Surrogate Explanations, Model-Agnostic Local Explanations, Locally Faithful Approximations, Yerel Yorumlanabilir Model-Bağımsız Açıklamalar | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes |
| Liên quan≠ | 5 | 2 | 4 |
| Tóm tắt≠ | 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. | LIME, introduced by Ribeiro, Singh, and Guestrin in 2016, explains the predictions of any black-box classifier or regressor by building a simple, locally faithful surrogate model around a single prediction of interest. Rather than explaining the global model, LIME focuses on why a specific instance was classified the way it was, making complex models such as deep neural networks and ensemble methods interpretable to end-users, domain experts, and auditors. | 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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