방법 비교

	선형 판별 분석 (LDA	나이브 베이즈
분야	통계학	머신러닝
계열	Hypothesis test	Machine learning
기원 연도	1936	1997
창시자	Ronald A. Fisher	Mitchell, T. M. (textbook treatment)
유형	Parametric linear classifier / dimensionality reduction	Probabilistic classifier (Bayes' theorem with conditional independence)
원전	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
별칭	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
관련	7	4
요약	Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA.	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.