השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| שיטת השכנים הקרובים ביותר (K-Nearest Neighbors)× | בייס נאיבי× | |
|---|---|---|
| תחום | למידת מכונה | למידת מכונה |
| משפחה | Machine learning | Machine learning |
| שנת המקור≠ | 1967 | 1997 |
| הוגה השיטה≠ | Cover, T.M. & Hart, P.E. | Mitchell, T. M. (textbook treatment) |
| סוג≠ | Instance-based (non-parametric) learning | Probabilistic classifier (Bayes' theorem with conditional independence) |
| מקור מכונן≠ | 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 |
| כינויים≠ | KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes |
| קשורות≠ | 5 | 4 |
| תקציר≠ | 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. |
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