পদ্ধতির তুলনা করুন

	ল্যাসো রিগ্রেশন	সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন
ক্ষেত্র	যন্ত্র শিখন	অর্থমিতি
পরিবার	Machine learning	Regression model
উদ্ভবের বছর	1996	2019
প্রবর্তক	Tibshirani, R.	Wooldridge (textbook treatment); classical least squares
ধরন	Regularized linear regression (L1 penalty)	Linear regression
মৌলিক উৎস	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
অপর নাম	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
সম্পর্কিত	4	5
সারসংক্ষেপ	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).