📘 An interesting initial book release by David Rossell on variable and model selection:
👉 davidrusi.github.io/modelSelecti...
it provides accessible material for students learning the fundamentals of high-dimensional model selection, and it documents the R package modelSelection (formerly mombf).
👉 davidrusi.github.io/modelSelecti...
it provides accessible material for students learning the fundamentals of high-dimensional model selection, and it documents the R package modelSelection (formerly mombf).
High-dimensional model choice. A hands-on take
High-dimensional model selection with the modelSelection R package
davidrusi.github.io
October 23, 2025 at 7:59 AM
📘 An interesting initial book release by David Rossell on variable and model selection:
👉 davidrusi.github.io/modelSelecti...
it provides accessible material for students learning the fundamentals of high-dimensional model selection, and it documents the R package modelSelection (formerly mombf).
👉 davidrusi.github.io/modelSelecti...
it provides accessible material for students learning the fundamentals of high-dimensional model selection, and it documents the R package modelSelection (formerly mombf).
What really matters when selecting an LLM for your enterprise?
We break down the hidden costs, real benefits, and open source myths that tech leaders must understand.
📖 Read the full article >> nearform.com/digital-comm...
#AIArchitecture #ModelSelection #OpenSourceAI
We break down the hidden costs, real benefits, and open source myths that tech leaders must understand.
📖 Read the full article >> nearform.com/digital-comm...
#AIArchitecture #ModelSelection #OpenSourceAI
April 26, 2025 at 3:03 PM
What really matters when selecting an LLM for your enterprise?
We break down the hidden costs, real benefits, and open source myths that tech leaders must understand.
📖 Read the full article >> nearform.com/digital-comm...
#AIArchitecture #ModelSelection #OpenSourceAI
We break down the hidden costs, real benefits, and open source myths that tech leaders must understand.
📖 Read the full article >> nearform.com/digital-comm...
#AIArchitecture #ModelSelection #OpenSourceAI
AWS Nordic news: We welcome AWS Bedrock to Stockholm - right now very limited modelselection (Amazon Nova Lite, Amazon Nova Micro, and Amazon Nova Pro) aws.amazon.com/about-aws/wh...
Amazon Bedrock now available in the Europe (Stockholm) region - AWS
Discover more about what's new at AWS with Amazon Bedrock now available in the Europe (Stockholm) region
aws.amazon.com
March 10, 2025 at 3:20 PM
AWS Nordic news: We welcome AWS Bedrock to Stockholm - right now very limited modelselection (Amazon Nova Lite, Amazon Nova Micro, and Amazon Nova Pro) aws.amazon.com/about-aws/wh...
New CRAN package modelSelection with initial version 1.0.3
#rstats
https://cran.r-project.org/package=modelSelection
#rstats
https://cran.r-project.org/package=modelSelection
CRAN: Package modelSelection
Model selection and averaging for regression, generalized linear models, generalized additive models, graphical models and mixtures, focusing on Bayesian model selection and information criteria (Bayesian information criterion etc.). See Rossell (2025) <<a href="https://doi.org/10.5281%2Fzenodo.17119597" target="_top">doi:10.5281/zenodo.17119597</a>> (see the URL field below for its URL) for a hands-on book describing the methods, examples and suggested citations if you use the package.
cran.r-project.org
September 21, 2025 at 2:02 PM
New CRAN package modelSelection with initial version 1.0.3
#rstats
https://cran.r-project.org/package=modelSelection
#rstats
https://cran.r-project.org/package=modelSelection
Updates on CRAN: atime (2025.9.30), cmsafvis (1.3.0), DominoDataR (0.3.0), inDAGO (1.0.3), mets (1.3.8), MLwrap (0.2.1), modelSelection (1.0.4), R2MLwiN (0.8-10), svglite (2.2.2), VSURF (1.2.1)
October 21, 2025 at 5:21 PM
Updates on CRAN: atime (2025.9.30), cmsafvis (1.3.0), DominoDataR (0.3.0), inDAGO (1.0.3), mets (1.3.8), MLwrap (0.2.1), modelSelection (1.0.4), R2MLwiN (0.8-10), svglite (2.2.2), VSURF (1.2.1)
AWS has released the new 360-Eval Framework, a structured and data-driven way to evaluate large language models. It helps teams move beyond “vibes-based” testing to compare models and make confident choices.
Read more: awsinsider.net/articles/202...
#AI #ModelSelection
Read more: awsinsider.net/articles/202...
#AI #ModelSelection
AWS Details How to Pick Right AI Model with 360-Eval Framework -- AWSInsider
AWS outlined a structured, metrics-based approach to choosing the right large language model for specific use cases, promoting its 360-Eval framework to replace informal “vibes-based” testing with dat...
awsinsider.net
October 24, 2025 at 1:59 PM
AWS has released the new 360-Eval Framework, a structured and data-driven way to evaluate large language models. It helps teams move beyond “vibes-based” testing to compare models and make confident choices.
Read more: awsinsider.net/articles/202...
#AI #ModelSelection
Read more: awsinsider.net/articles/202...
#AI #ModelSelection
Model Selection and Model Simplification using R
Choose, compare, and refine models from AIC and BIC to cross-validation and averaging
Instructor: Dr Mark Andrews
9–10 Dec
prstats.org/course/model-selection-and-model-simplification-msms06
#Statistics #Rstats #DataAnalysis #ModelSelection #Bayesian
Choose, compare, and refine models from AIC and BIC to cross-validation and averaging
Instructor: Dr Mark Andrews
9–10 Dec
prstats.org/course/model-selection-and-model-simplification-msms06
#Statistics #Rstats #DataAnalysis #ModelSelection #Bayesian
Model Selection and Model Simplification | PR Statistics
This two-day course offers practical, hands-on training in statistical model selection, comparison, and evaluation using R. Participants will learn to choose among competing models, handle multiple pr...
prstats.org
November 10, 2025 at 11:18 AM
Model Selection and Model Simplification using R
Choose, compare, and refine models from AIC and BIC to cross-validation and averaging
Instructor: Dr Mark Andrews
9–10 Dec
prstats.org/course/model-selection-and-model-simplification-msms06
#Statistics #Rstats #DataAnalysis #ModelSelection #Bayesian
Choose, compare, and refine models from AIC and BIC to cross-validation and averaging
Instructor: Dr Mark Andrews
9–10 Dec
prstats.org/course/model-selection-and-model-simplification-msms06
#Statistics #Rstats #DataAnalysis #ModelSelection #Bayesian
Chainforge Philosophy #1: The silver bullet "All-in-one” Model is a myth. 🦄 Every LLM has inevitable strengths & weaknesses based on its architecture & data. 🧭 #NoFreeLunch #ModelSelection #AIPhilosophy
October 8, 2025 at 2:30 PM
Chainforge Philosophy #1: The silver bullet "All-in-one” Model is a myth. 🦄 Every LLM has inevitable strengths & weaknesses based on its architecture & data. 🧭 #NoFreeLunch #ModelSelection #AIPhilosophy
Users face growing complexity in model selection. GPT-5 offers regular, mini, and nano versions, each with four reasoning levels. This prompts questions: is it better to fine-tune prompts or switch between models/levels for optimal task performance? #ModelSelection 5/5
August 8, 2025 at 10:00 AM
Users face growing complexity in model selection. GPT-5 offers regular, mini, and nano versions, each with four reasoning levels. This prompts questions: is it better to fine-tune prompts or switch between models/levels for optimal task performance? #ModelSelection 5/5
🚨 New Video Alert! 🎬
Bias, Variance, Model Selection - I break it all down in my latest video! Level up your ML skills & understand how models make decisions:
youtu.be/NRaRl36M9kM
#machinelearning #AI #datascience #bias #variance #modelselection
March 22, 2024 at 7:40 AM
🚨 New Video Alert! 🎬
Bias, Variance, Model Selection - I break it all down in my latest video! Level up your ML skills & understand how models make decisions:
youtu.be/NRaRl36M9kM
#machinelearning #AI #datascience #bias #variance #modelselection
Hands on #Statistics education! Registration is live for 4 virtual summer workshops in conjunction with eastonsnook.com. All welcome. #LongitudinalDataAnalysis #ModelSelection #SampleSize #ScaleDevelopment #GradSchool #AcademicSky #StatisticalProgramming #SAS #STATA #SPSS #MaybeR #MaybePy
Easton's Nook | An Urban Writing and Arts Retreat Space
Easton's Nook is an intimate urban writing and arts retreat space where scholars, artists, and activists can meet, and can pursue their work individually or as a part of small, supportive, collaborati...
eastonsnook.com
May 22, 2025 at 12:41 AM
Hands on #Statistics education! Registration is live for 4 virtual summer workshops in conjunction with eastonsnook.com. All welcome. #LongitudinalDataAnalysis #ModelSelection #SampleSize #ScaleDevelopment #GradSchool #AcademicSky #StatisticalProgramming #SAS #STATA #SPSS #MaybeR #MaybePy
PSISLOOCVFAQ - *cough* - bayesian cross-validation frequently asked questions answered: https://avehtari.github.io/modelselection/CV-FAQ.html#23_What_is_the_relationship_between_AIC,_DIC,_WAIC_and_LOO-CV
Cross-validation FAQ
avehtari.github.io
July 31, 2023 at 2:01 PM
PSISLOOCVFAQ - *cough* - bayesian cross-validation frequently asked questions answered: https://avehtari.github.io/modelselection/CV-FAQ.html#23_What_is_the_relationship_between_AIC,_DIC,_WAIC_and_LOO-CV
#statstab #393 Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements [actual post]
Thoughts: #392 has the comments, but this is where the magic happens.
#modelselection #modelcomparison #variance #effectsize #tutorial
www.fharrell.com/post/addvalue/
Thoughts: #392 has the comments, but this is where the magic happens.
#modelselection #modelcomparison #variance #effectsize #tutorial
www.fharrell.com/post/addvalue/
Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements – Statistical Thinking
Researchers have used contorted, inefficient, and arbitrary analyses to demonstrated added value in biomarkers, genes, and new lab measurements. Traditional statistical measures have always been up to...
www.fharrell.com
July 23, 2025 at 6:48 PM
#statstab #393 Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements [actual post]
Thoughts: #392 has the comments, but this is where the magic happens.
#modelselection #modelcomparison #variance #effectsize #tutorial
www.fharrell.com/post/addvalue/
Thoughts: #392 has the comments, but this is where the magic happens.
#modelselection #modelcomparison #variance #effectsize #tutorial
www.fharrell.com/post/addvalue/
Very significant special issue with brilliant editorial team #ModelSelection #MarketingResearch
JMM Special Issue #CallForPapers: Model Selection in Marketing Research. Guest Editors: Marko Sarstedt, Francesca Magno, Vasilica-Maria Margalina, Fabio Cassia and Christian M. Ringle (Deadline for Submissions 1 June 2026)
www.jmmnews.com/model-select...
www.jmmnews.com/model-select...
Model Selection in Marketing Research - Journal of Marketing Management
JMM Special Issue: Model Selection in Marketing Research. Guest Editors: Marko Sarstedt, Francesca Magno, Vasilica-Maria Margalina, Fabio Cassia and Christian M. Ringle
www.jmmnews.com
October 27, 2025 at 9:08 AM
Very significant special issue with brilliant editorial team #ModelSelection #MarketingResearch
Right ML model = Better results 🚀
⚡ Fast: Linear Regression, Naive Bayes, Decision Trees
⏳ Moderate: Logistic Regression, K-Means, PCA
🐢 Slow: Random Forest, Gradient Boosting, DNN
💾 Data size | 💪 Resources | 🎯 Performance vs. speed
#MachineLearning #AI #ModelSelection
⚡ Fast: Linear Regression, Naive Bayes, Decision Trees
⏳ Moderate: Logistic Regression, K-Means, PCA
🐢 Slow: Random Forest, Gradient Boosting, DNN
💾 Data size | 💪 Resources | 🎯 Performance vs. speed
#MachineLearning #AI #ModelSelection
February 6, 2025 at 9:52 AM
Right ML model = Better results 🚀
⚡ Fast: Linear Regression, Naive Bayes, Decision Trees
⏳ Moderate: Logistic Regression, K-Means, PCA
🐢 Slow: Random Forest, Gradient Boosting, DNN
💾 Data size | 💪 Resources | 🎯 Performance vs. speed
#MachineLearning #AI #ModelSelection
⚡ Fast: Linear Regression, Naive Bayes, Decision Trees
⏳ Moderate: Logistic Regression, K-Means, PCA
🐢 Slow: Random Forest, Gradient Boosting, DNN
💾 Data size | 💪 Resources | 🎯 Performance vs. speed
#MachineLearning #AI #ModelSelection
#statstab #358 What are some of the problems with stepwise regression?
Thoughts: Model selection is not an easy task, but maybe don't naively try step wise reg.
#stepwise #regression #QRPs #issues #phacking #modelselection #bias
www.stata.com/support/faqs...
Thoughts: Model selection is not an easy task, but maybe don't naively try step wise reg.
#stepwise #regression #QRPs #issues #phacking #modelselection #bias
www.stata.com/support/faqs...
Stata | FAQ: Problems with stepwise regression
What are some of the problems with stepwise regression?
www.stata.com
June 4, 2025 at 2:48 PM
#statstab #358 What are some of the problems with stepwise regression?
Thoughts: Model selection is not an easy task, but maybe don't naively try step wise reg.
#stepwise #regression #QRPs #issues #phacking #modelselection #bias
www.stata.com/support/faqs...
Thoughts: Model selection is not an easy task, but maybe don't naively try step wise reg.
#stepwise #regression #QRPs #issues #phacking #modelselection #bias
www.stata.com/support/faqs...
Can't fix it? Switch models! 🔄
Claude, OpenAI, Gemini – different models succeed where others fail.
Once you find the source, reset and give precise instructions on a clean codebase.
#AIMultitool #AICoding #ModelSelection
Claude, OpenAI, Gemini – different models succeed where others fail.
Once you find the source, reset and give precise instructions on a clean codebase.
#AIMultitool #AICoding #ModelSelection
April 26, 2025 at 6:06 PM
Can't fix it? Switch models! 🔄
Claude, OpenAI, Gemini – different models succeed where others fail.
Once you find the source, reset and give precise instructions on a clean codebase.
#AIMultitool #AICoding #ModelSelection
Claude, OpenAI, Gemini – different models succeed where others fail.
Once you find the source, reset and give precise instructions on a clean codebase.
#AIMultitool #AICoding #ModelSelection
New on CRAN: modelSelection (1.0.3). View at https://CRAN.R-project.org/package=modelSelection
modelSelection: High-Dimensional Model Selection
Model selection and averaging for regression, generalized linear models, generalized additive models, graphical models and mixtures, focusing on Bayesian model selection and information criteria (Bayesian information criterion etc.). See Rossell (2025) <<a href="https://doi.org/10.5281%2Fzenodo.17119597" target="_top">doi:10.5281/zenodo.17119597</a>> (see the URL field below for its URL) for a hands-on book describing the methods, examples and suggested citations if you use the package.
CRAN.R-project.org
September 21, 2025 at 5:04 PM
New on CRAN: modelSelection (1.0.3). View at https://CRAN.R-project.org/package=modelSelection
#statstab #392 Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements
Thoughts: Forums can be great for asking the author for exact answers to complex questions
#modelselection #causalinference #prediction #bias #information
discourse.datamethods.org/t/statistica...
Thoughts: Forums can be great for asking the author for exact answers to complex questions
#modelselection #causalinference #prediction #bias #information
discourse.datamethods.org/t/statistica...
Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements
This topic is for discussions about Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements
discourse.datamethods.org
July 22, 2025 at 8:24 PM
#statstab #392 Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements
Thoughts: Forums can be great for asking the author for exact answers to complex questions
#modelselection #causalinference #prediction #bias #information
discourse.datamethods.org/t/statistica...
Thoughts: Forums can be great for asking the author for exact answers to complex questions
#modelselection #causalinference #prediction #bias #information
discourse.datamethods.org/t/statistica...