Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| К-най-близки съседи× | Наивен Бейс× | Случайна гора× | |
|---|---|---|---|
| Област | Машинно обучение | Машинно обучение | Машинно обучение |
| Семейство | Machine learning | Machine learning | Machine learning |
| Година на възникване≠ | 1967 | 1997 | 2001 |
| Създател≠ | Cover, T.M. & Hart, P.E. | Mitchell, T. M. (textbook treatment) | Breiman, L. |
| Тип≠ | Instance-based (non-parametric) learning | Probabilistic classifier (Bayes' theorem with conditional independence) | Ensemble (bagging of decision trees) |
| Основополагащ източник≠ | Cover, T.M. & Hart, P.E. (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 | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Други названия≠ | KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Свързани≠ | 5 | 4 | 4 |
| Резюме≠ | K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values. | 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. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateНабор от данни ↗ |
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