পদ্ধতির তুলনা করুন

	ল্যাসো রিগ্রেশন	প্রধান উপাদান বিশ্লেষণ
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1996	2002
প্রবর্তক	Tibshirani, R.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
ধরন	Regularized linear regression (L1 penalty)	Unsupervised dimensionality reduction
মৌলিক উৎস	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
অপর নাম	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
সম্পর্কিত	4	3
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.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.