Salīdzināt metodes

	Lineārās diskriminanta analīze (LDA	Random Forest
Nozare	Statistika	Mašīnmācīšanās
Saime	Hypothesis test	Machine learning
Izcelsmes gads	1936	2001
Autors	Ronald A. Fisher	Breiman, L.
Tips	Parametric linear classifier / dimensionality reduction	Ensemble (bagging of decision trees)
Pirmavots	Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Citi nosaukumi	LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Saistītās	7	4
Kopsavilkums	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.