Elastic Net Regression |
| Description: | Elastic Net regression combines the L1 penalty of Lasso with the L2 penalty of Ridge regression. It minimizes the objective function: (1/2n) · ||y - Xß||² + ?1·||ß||1 + ?2·||ß||2². The L1 term (?1 times the sum of absolute coefficients) drives sparsity by shrinking some coefficients exactly to zero, performing variable selection like Lasso. The L2 term (?2 times the sum of squared coefficients) handles correlated predictors gracefully by encouraging them to share weight rather than arbitrarily picking one, which is Lasso's main weakness. |
| Filename: | elasticnetmlr.zip |
| ID: | 9826 |
| Author: | Namir Shammas |
| Downloaded file size: | 8,298 bytes |
| Size on calculator: | 23 KB |
| Platforms: | Prime |
| User rating: | Not yet rated (you must be logged in to vote) |
| Primary category: | Math |
| Languages: | ENG |
| File date: | 2026-03-05 11:22:31 |
| Creation date: | 2026-03-05 |
| Source code: | Included |
| Download count: | 2 |
| Version history: | 2026-08-11: Added to site
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22684 2026-03-05 12:22 elasticNetMlr.hpprgm
17337 2026-03-05 12:22 elasticNetMlr.html
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40021 2 files |
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