পদ্ধতির তুলনা করুন

	ইলাস্টিক নেট	ল্যাসো রিগ্রেশন	লজিস্টিক রিগ্রেশন
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন	গবেষণা পরিসংখ্যান
পরিবার	Machine learning	Machine learning	Process / pipeline
উদ্ভবের বছর	2005	1996	1958
প্রবর্তক	Zou, H. & Hastie, T.	Tibshirani, R.	David Roxbee Cox
ধরন	Regularized linear regression (L1 + L2 penalty)	Regularized linear regression (L1 penalty)	Method
মৌলিক উৎস	Zou, H. & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 67(2), 301–320.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
অপর নাম	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	logit model, binomial logistic regression, LR
সম্পর্কিত	4	4	3
সারসংক্ষেপ	Elastic Net is a regularized linear regression method introduced by Zou and Hastie in 2005 that blends the LASSO (L1) and Ridge (L2) penalties, so it performs variable selection and coefficient shrinkage at the same time. It is designed for predictive and explanatory modelling on data with many, possibly correlated, predictors.	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.