Difference between revisions of "Elastic Net Regression"
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| − | |keywords=artificial, intelligence, machine, learning, models | + | |keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools |
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| − | [ | + | [https://www.youtube.com/results?search_query=Elastic+Net+Regression+artificial+intelligence YouTube 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]] ... [[Train, Validate, and Test]] |
** [[...predict values]] | ** [[...predict values]] | ||
| − | |||
* [[Regression]] Analysis | * [[Regression]] Analysis | ||
* [[Regularization]] | * [[Regularization]] | ||
** [[Ridge Regression]] | ** [[Ridge Regression]] | ||
| − | *** [ | + | *** [https://towardsdatascience.com/ridge-and-lasso-regression-a-complete-guide-with-python-scikit-learn-e20e34bcbf0b Ridge and Lasso Regression: A Complete Guide with Python Scikit-Learn | Saptashwa - Towards Data Science] |
** [[Lasso Regression]] | ** [[Lasso Regression]] | ||
| − | * [[Math for Intelligence]] | + | * [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]] |
* [[Overfitting Challenge]] | * [[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. [[ | + | 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. [[https://statquest.org/video-index/ StatQuest] |
<youtube>1dKRdX9bfIo</youtube> | <youtube>1dKRdX9bfIo</youtube> | ||
Latest revision as of 22:01, 5 March 2024
YouTube search... ...Google search
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... 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
