পদ্ধতির তুলনা করুন

	রৈখিক বৈষম্যমূলক বিশ্লেষণ (LDA	নেইভ বেইজ
ক্ষেত্র	পরিসংখ্যান	যন্ত্র শিখন
পরিবার	Hypothesis test	Machine learning
উদ্ভবের বছর	1936	1997
প্রবর্তক	Ronald A. Fisher	Mitchell, T. M. (textbook treatment)
ধরন	Parametric linear classifier / dimensionality reduction	Probabilistic classifier (Bayes' theorem with conditional independence)
মৌলিক উৎস	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
অপর নাম	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
সম্পর্কিত	7	4
সারসংক্ষেপ	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.