পদ্ধতির তুলনা করুন

	দ্বিঘাত বৈষম্যমূলক বিশ্লেষণ (QDA)	নেইভ বেইজ
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Latent structure	Machine learning
উদ্ভবের বছর	1939	1997
প্রবর্তক	Classical Gaussian discriminant analysis (Fisher / Welch lineage)	Mitchell, T. M. (textbook treatment)
ধরন	Generative Gaussian classifier	Probabilistic classifier (Bayes' theorem with conditional independence)
মৌলিক উৎস	Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer. ISBN: 978-0-387-84857-0	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
অপর নাম	QDA, quadratic classifier, kuadratik diskriminant analizi	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
সম্পর্কিত	2	4
সারসংক্ষেপ	Quadratic discriminant analysis is a generative classifier that models each class with its own multivariate Gaussian distribution, allowing each class a separate covariance matrix. Unlike linear discriminant analysis, which assumes a shared covariance and yields linear boundaries, QDA's per-class covariances produce curved (quadratic) decision boundaries, letting it capture differences in the spread and orientation of the classes.	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.