विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| कारक विश्लेषण× | लासो रिग्रेशन× | |
|---|---|---|
| क्षेत्र≠ | अनुसंधान सांख्यिकी | मशीन अधिगम |
| परिवार≠ | Process / pipeline | Machine learning |
| उद्भव वर्ष≠ | 1931 | 1996 |
| प्रवर्तक≠ | Louis Leon Thurstone | Tibshirani, R. |
| प्रकार≠ | Method | Regularized linear regression (L1 penalty) |
| मौलिक स्रोत≠ | Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press. DOI ↗ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| उपनाम≠ | EFA, CFA, latent variable modeling | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| संबंधित≠ | 3 | 4 |
| सारांश≠ | Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate 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. |
| ScholarGateडेटासेट ↗ |
|
|