Difference between revisions of "Elastic Net Regression"
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Elastic-Net Regression is combines Lasso Regression with Ridge Regression to give you the best of both worlds. It works well when there are lots of useless variables that need to be removed from the equation and it works well when there are lots of useful variables that need to be retained. And it does better than either one when it comes to handling correlated variables. [[http://statquest.org/video-index/ StatQuest] | Elastic-Net Regression is combines Lasso Regression with Ridge Regression to give you the best of both worlds. It works well when there are lots of useless variables that need to be removed from the equation and it works well when there are lots of useful variables that need to be retained. And it does better than either one when it comes to handling correlated variables. [[http://statquest.org/video-index/ StatQuest] | ||
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Revision as of 00:49, 13 July 2019
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- AI Solver
- Capabilities
- Linear Regression
- Regularization
- Logistic Regression (LR)
- Statistics for Intelligence
- Overfitting Challenge
Elastic-Net Regression is combines Lasso Regression with Ridge Regression to give you the best of both worlds. It works well when there are lots of useless variables that need to be removed from the equation and it works well when there are lots of useful variables that need to be retained. And it does better than either one when it comes to handling correlated variables. [StatQuest
