विधियों की तुलना करें

	क्वाड्रैटिक डिस्क्रिमिनेंट एनालिसिस (QDA)	रैखिक विभेदक विश्लेषण (LDA)
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Latent structure	Latent structure
उद्भव वर्ष	1939	1936
प्रवर्तक	Classical Gaussian discriminant analysis (Fisher / Welch lineage)	Fisher, R. A.
प्रकार	Generative Gaussian classifier	Supervised dimensionality reduction and linear classifier
मौलिक स्रोत	Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer. ISBN: 978-0-387-84857-0	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.
उपनाम	QDA, quadratic classifier, kuadratik diskriminant analizi	LDA, Fisher's discriminant analysis, Fisher linear discriminant, normal discriminant analysis
संबंधित	2	4
सारांश	Quadratic discriminant analysis is a generative classifier that models each class with its own multivariate Gaussian distribution, allowing each class a separate covariance matrix. Unlike linear discriminant analysis, which assumes a shared covariance and yields linear boundaries, QDA's per-class covariances produce curved (quadratic) decision boundaries, letting it capture differences in the spread and orientation of the classes.	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.