Elastic Net Regression - Revision history

BPeat at 03:01, 6 March 2024

2024-03-06T03:01:53Z

BPeat at 00:27, 13 July 2023

2023-07-13T00:27:45Z

BPeat at 01:33, 8 July 2023

2023-07-08T01:33:20Z

BPeat at 15:57, 4 June 2023

2023-06-04T15:57:23Z

BPeat: Text replacement - "http:" to "https:"

2023-03-28T15:41:24Z

Text replacement - "http:" to "https:"

BPeat at 14:51, 12 October 2020

2020-10-12T14:51:16Z

BPeat at 06:47, 13 July 2019

2019-07-13T06:47:39Z

BPeat at 06:23, 13 July 2019

2019-07-13T06:23:42Z

BPeat at 06:23, 13 July 2019

2019-07-13T06:23:24Z

BPeat at 05:55, 13 July 2019

2019-07-13T05:55:11Z

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Elastic+Net+Regression+machine+learning+ML ...Google search]
-* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
 ** [[...predict values]]
 * [[Regression]] Analysis

@@ Line 17: / Line 17: @@
 [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]]
 * [[Regression]] Analysis

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|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
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [https://www.youtube.com/results?search_query=Elastic+Net+Regression+artificial+intelligence YouTube search...]
@@ Line 10: / Line 19: @@
 * [[AI Solver]]
 ** [[...predict values]]
 * [[Regression]] Analysis
 * [[Regularization]]
@@ Line 16: / Line 24: @@
 *** [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]]
-* [[Math for Intelligence]] ... [[Finding Paul Revere]]
+* [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]]
 * [[Overfitting Challenge]]

@@ Line 16: / Line 16: @@
 *** [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]]
-* [[Math for Intelligence]]
+* [[Math for Intelligence]] ... [[Finding Paul Revere]]
 * [[Overfitting Challenge]]

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Elastic+Net+Regression+artificial+intelligence YouTube search...]
+[https://www.youtube.com/results?search_query=Elastic+Net+Regression+artificial+intelligence YouTube search...]
-[http://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]]
@@ Line 14: / Line 14: @@
 * [[Regularization]]
 ** [[Ridge Regression]]
-*** [http://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]
+*** [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]]
 * [[Math 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. [[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. [[https://statquest.org/video-index/ StatQuest]
 <youtube>1dKRdX9bfIo</youtube>

← Older revision		Revision as of 06:47, 13 July 2019
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	<youtube>1dKRdX9bfIo</youtube>		<youtube>1dKRdX9bfIo</youtube>
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−	~~http://i.stack.imgur.com/vRJ4p.png~~
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−	* Left plot: Elastic net gradient vs. bridge regression weight along each dimension - The x axis represents one component of a set of weights w∗ selected by bridge regression. The y axis represents the corresponding component of the elastic net gradient, evaluated at w∗. Note that the weights are multidimensional, but we're just looking at the weights/gradient along a single dimension.
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−	* Right plot: Elastic net changes to bridge regression weights (2d) - Each point represents a set of 2d weights w∗ selected by bridge regression. For each choice of w∗, a vector is plotted pointing in the direction opposite the elastic net gradient, with magnitude proportional to that of the gradient. That is, the plotted vectors show how the elastic net wants to change the bridge regression solution. [http://stats.stackexchange.com/questions/224531/bridge-penalty-vs-elastic-net-regularization Bridge penalty vs. Elastic Net regularization \| user20160]

← Older revision		Revision as of 06:23, 13 July 2019
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	<youtube>1dKRdX9bfIo</youtube>		<youtube>1dKRdX9bfIo</youtube>
−	~~<youtube>jbwSCwoT51M</youtube>~~