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| Online Naive Bayes× | Drzewo decyzyjne online× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2000s | 2000 |
| Twórca≠ | Adapted from traditional Naive Bayes; incremental form established by the data-stream mining community (Domingos, Hulten, and others, circa 2000) | Domingos, P. & Hulten, G. |
| Typ≠ | Probabilistic classifier (online/incremental) | Incremental supervised classifier |
| Źródło pierwotne≠ | Domingos, P. & Hulten, G. (2000). Mining high-speed data streams. Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 71–80. ACM. DOI ↗ | Domingos, P., & Hulten, G. (2000). Mining very fast data streams. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 71–80). ACM. link ↗ |
| Inne nazwy | Incremental Naive Bayes, Streaming Naive Bayes, Naive Bayes with partial_fit, Online NB | Hoeffding Tree, VFDT, Very Fast Decision Tree, incremental decision tree |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | Online Naive Bayes is an incremental adaptation of the classical Naive Bayes classifier that updates its class-conditional statistics one observation (or one mini-batch) at a time, making it well suited to data streams, very large datasets that cannot be held in memory, and settings where the model must adapt continuously as new labeled examples arrive. | An Online Decision Tree is a decision tree that grows incrementally from a continuous stream of data without revisiting past examples. The dominant algorithm, the Hoeffding Tree (VFDT), uses the Hoeffding bound to decide when enough examples have been seen at a node to split it confidently, enabling scalable, real-time classification on potentially infinite data streams. |
| ScholarGateZbiór danych ↗ |
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