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| Stablo odlučivanja× | Naive Bayes× | Stroj potpornih vektora (klasifikacija)× | |
|---|---|---|---|
| Područje | Strojno učenje | Strojno učenje | Strojno učenje |
| Obitelj | Machine learning | Machine learning | Machine learning |
| Godina nastanka≠ | 1984 | 1997 | 1995 |
| Tvorac≠ | Breiman, Friedman, Olshen & Stone | Mitchell, T. M. (textbook treatment) | Cortes, C. & Vapnik, V. |
| Vrsta≠ | Recursive partitioning (if-then rules) | Probabilistic classifier (Bayes' theorem with conditional independence) | Maximum-margin classifier (kernel method) |
| Temeljni izvor≠ | 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 | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Drugi nazivi≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Srodne≠ | 5 | 4 | 5 |
| Sažetak≠ | 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. | The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data. |
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