Difference between revisions of "Elastic Net Regression"
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[https://www.google.com/search?q=Elastic+Net+Regression+machine+learning+ML ...Google search] | [https://www.google.com/search?q=Elastic+Net+Regression+machine+learning+ML ...Google search] | ||
| − | * [[AI Solver]] | + | * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]] |
** [[...predict values]] | ** [[...predict values]] | ||
* [[Regression]] Analysis | * [[Regression]] Analysis | ||
Revision as of 19:27, 12 July 2023
YouTube search... ...Google search
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Optimizer ... Train, Validate, and Test
- Regression Analysis
- Regularization
- Math for Intelligence ... Finding Paul Revere ... Social Network Analysis (SNA) ... Dot Product ... Kernel Trick
- 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
