Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Koku lēmumu pieņemšana (Decision Tree)× | Naive Bayes× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1984 | 1997 |
| Autors≠ | Breiman, Friedman, Olshen & Stone | Mitchell, T. M. (textbook treatment) |
| Tips≠ | Recursive partitioning (if-then rules) | Probabilistic classifier (Bayes' theorem with conditional independence) |
| Pirmavots≠ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 |
| Citi nosaukumi | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | 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. | 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. |
| ScholarGateDatu kopa ↗ |
|
|