পদ্ধতির তুলনা করুন

	রৈখিক বৈষম্যমূলক বিশ্লেষণ (LDA	কে-নিয়ারেস্ট নেইবারস
ক্ষেত্র	পরিসংখ্যান	যন্ত্র শিখন
পরিবার	Hypothesis test	Machine learning
উদ্ভবের বছর	1936	1967
প্রবর্তক	Ronald A. Fisher	Cover, T.M. & Hart, P.E.
ধরন	Parametric linear classifier / dimensionality reduction	Instance-based (non-parametric) learning
মৌলিক উৎস	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.	Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.
অপর নাম	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning
সম্পর্কিত	7	5
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.	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.