विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| के-निकटतम पड़ोसी (K-Nearest Neighbors)× | निर्णय वृक्ष× | Naive Bayes× | |
|---|---|---|---|
| क्षेत्र | मशीन अधिगम | मशीन अधिगम | मशीन अधिगम |
| परिवार | Machine learning | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 1967 | 1984 | 1997 |
| प्रवर्तक≠ | Cover, T.M. & Hart, P.E. | Breiman, Friedman, Olshen & Stone | Mitchell, T. M. (textbook treatment) |
| प्रकार≠ | Instance-based (non-parametric) learning | Recursive partitioning (if-then rules) | Probabilistic classifier (Bayes' theorem with conditional independence) |
| मौलिक स्रोत≠ | Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI ↗ | 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 |
| उपनाम≠ | KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes |
| संबंधित≠ | 5 | 5 | 4 |
| सारांश≠ | 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. | 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. |
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