Сравнение методов

	Линейный дискриминантный анализ (LDA)	Случайный лес
Область	Машинное обучение	Машинное обучение
Семейство	Latent structure	Machine learning
Год появления	1936	2001
Автор метода	Fisher, R. A.	Breiman, L.
Тип	Supervised dimensionality reduction and linear classifier	Ensemble (bagging of decision trees)
Основополагающий источник	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.
Другие названия	LDA, Fisher's discriminant analysis, Fisher linear discriminant, normal discriminant analysis	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Связанные	4	4
Сводка	Linear Discriminant Analysis is a supervised method for dimensionality reduction and classification, introduced by Ronald A. Fisher in 1936, that finds linear combinations of features which maximally separate predefined classes while preserving as much class-discriminatory information as possible. It simultaneously serves as a feature-projection technique and a probabilistic classifier, making it one of the foundational methods in pattern recognition and statistical learning.	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.