Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Naive Bayes× | Koku lēmumu pieņemšana (Decision Tree)× | Logistiskā regresija× | |
|---|---|---|---|
| Nozare≠ | Mašīnmācīšanās | Mašīnmācīšanās | Pētniecības statistika |
| Saime≠ | Machine learning | Machine learning | Process / pipeline |
| Izcelsmes gads≠ | 1997 | 1984 | 1958 |
| Autors≠ | Mitchell, T. M. (textbook treatment) | Breiman, Friedman, Olshen & Stone | David Roxbee Cox |
| Tips≠ | Probabilistic classifier (Bayes' theorem with conditional independence) | Recursive partitioning (if-then rules) | Method |
| Pirmavots≠ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Citi nosaukumi≠ | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | logit model, binomial logistic regression, LR |
| Saistītās≠ | 4 | 5 | 3 |
| Kopsavilkums≠ | 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. | 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. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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