Σύγκριση μεθόδων

	Ποσοστιαία Παλινδρόμηση (Μη Παραμετρικές Παραλλαγές)	Παλινδρόμηση Lasso
Πεδίο	Στατιστική	Μηχανική Μάθηση
Οικογένεια	Regression model	Machine learning
Έτος προέλευσης	1978	1996
Δημιουργός	Koenker & Bassett	Tibshirani, R.
Τύπος	Quantile regression (nonparametric variants)	Regularized linear regression (L1 penalty)
Θεμελιώδης πηγή	Koenker, R. & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Εναλλακτικές ονομασίες	quantile regression, median regression, distribution-free quantile regression, Kantil Regresyon (Nonparametric Varyantlar)	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Συναφείς	5	4
Σύνοψη	Quantile regression, introduced by Koenker and Bassett in 1978, models a chosen conditional quantile (such as the median or the 25th and 75th percentiles) of a continuous outcome rather than its mean. Its nonparametric variants fit these quantile relationships without assuming a distribution for the errors, making them a robust complement to mean-based regression on skewed data.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.