#modelevaluation — Public Fediverse posts

About metrics for measuring agreement on regression on continuous datasets:
Reasons to avoid R² and use RMSE instead: https://feat.engineering/03-Review_of_the_Modeling_Process.html#sec-reg-metrics

From Max Kuhn @topepo, Kjell Johnson (2026), "Feature Engineering and Selection: A Practical Approach for Predictive Models"

#prediction #dataDev #modelEvaluation #regression #modelling #linearRegression #modeling #probability #probabilities #statistics #stats #gotcha

About metrics for measuring agreement on regression on continuous datasets:
Reasons to avoid R² and use RMSE instead: https://feat.engineering/03-Review_of_the_Modeling_Process.html#sec-reg-metrics

From Max Kuhn @topepo, Kjell Johnson (2026), "Feature Engineering and Selection: A Practical Approach for Predictive Models"

#prediction #dataDev #modelEvaluation #regression #modelling #linearRegression #modeling #probability #probabilities #statistics #stats #gotcha

About metrics for measuring agreement on regression on continuous datasets:
Reasons to avoid R² and use RMSE instead: https://feat.engineering/03-Review_of_the_Modeling_Process.html#sec-reg-metrics

From Max Kuhn @topepo, Kjell Johnson (2026), "Feature Engineering and Selection: A Practical Approach for Predictive Models"

#prediction #dataDev #modelEvaluation #regression #modelling #linearRegression #modeling #probability #probabilities #statistics #stats #gotcha

About metrics for measuring agreement on regression on continuous datasets:
Reasons to avoid R² and use RMSE instead: https://feat.engineering/03-Review_of_the_Modeling_Process.html#sec-reg-metrics

From Max Kuhn @topepo, Kjell Johnson (2026), "Feature Engineering and Selection: A Practical Approach for Predictive Models"

#prediction #dataDev #modelEvaluation #regression #modelling #linearRegression #modeling #probability #probabilities #statistics #stats #gotcha

About metrics for measuring agreement on regression on continuous datasets:
Reasons to avoid R² and use RMSE instead: https://feat.engineering/03-Review_of_the_Modeling_Process.html#sec-reg-metrics

From Max Kuhn @topepo, Kjell Johnson (2026), "Feature Engineering and Selection: A Practical Approach for Predictive Models"

#prediction #dataDev #modelEvaluation #regression #modelling #linearRegression #modeling #probability #probabilities #statistics #stats #gotcha

#NLP #LLMs #MentalHealth #ClinicalNLP #DigitalHealth #ResponsibleAI #NLProc #AIevaluation #ModelEvaluation #TrustworthyAI #Safety #Equity #HumanCenteredAI

OpenAI Tries To Measure Whether AI Reasoning Can Be Trusted

https://www.olamnews.com/research-report/3315/monitorability-chain-of-thought-evaluations/

@data @datadon 🧵

Accuracy! To counter regression dilution, a method is to add a constraint on the statistical modeling.
Regression Redress restrains bias by segregating the residual values.
My article: http://data.yt/kit/regression-redress.html

#bias #modeling #dataDev #AIDev #modelEvaluation #regression #modelling #dataLearning #linearRegression #probability #probabilities #statistics #stats #correctionRatio #ML #distributions #accuracy #RegressionRedress #Python #RStats

@data @datadon 🧵

Accuracy! To counter regression dilution, a method is to add a constraint on the statistical modeling.
Regression Redress restrains bias by segregating the residual values.
My article: http://data.yt/kit/regression-redress.html

#bias #modeling #dataDev #AIDev #modelEvaluation #regression #modelling #dataLearning #linearRegression #probability #probabilities #statistics #stats #correctionRatio #ML #distributions #accuracy #RegressionRedress #Python #RStats

@[email protected] @[email protected] 🧵

Accuracy! To counter regression dilution, a method is to add a constraint on the statistical modeling.
Regression Redress restrains bias by segregating the residual values.
My article: http://data.yt/kit/regression-redress.html

#bias #modeling #dataDev #AIDev #modelEvaluation #regression #modelling #dataLearning #linearRegression #probability #probabilities #statistics #stats #correctionRatio #ML #distributions #accuracy #RegressionRedress #Python #RStats

@data @datadon 🧵

Accuracy! To counter regression dilution, a method is to add a constraint on the statistical modeling.
Regression Redress restrains bias by segregating the residual values.
My article: http://data.yt/kit/regression-redress.html

#bias #modeling #dataDev #AIDev #modelEvaluation #regression #modelling #dataLearning #linearRegression #probability #probabilities #statistics #stats #correctionRatio #ML #distributions #accuracy #RegressionRedress #Python #RStats

@data @datadon 🧵

Accuracy! To counter regression dilution, a method is to add a constraint on the statistical modeling.
Regression Redress restrains bias by segregating the residual values.
My article: http://data.yt/kit/regression-redress.html

#bias #modeling #dataDev #AIDev #modelEvaluation #regression #modelling #dataLearning #linearRegression #probability #probabilities #statistics #stats #correctionRatio #ML #distributions #accuracy #RegressionRedress #Python #RStats

@data @datadon 🧵

How to assess a statistical model?
How to choose between variables?

Pearson's #correlation is irrelevant if you suspect that the relationship is not a straight line.

If monotonic relationship:
"#Spearman’s rho is particularly useful for small samples where weak correlations are expected, as it can detect subtle monotonic trends." It is "widespread across disciplines where the measurement precision is not guaranteed".
"#Kendall’s Tau-b is less affected [than Spearman’s rho] by outliers in the data, making it a robust option for datasets with extreme values."
Ref: https://statisticseasily.com/kendall-tau-b-vs-spearman/

#normality #normalDistribution #modeling #dataDev #AIDev #ML #modelEvaluation #regression #modelling #dataLearning #featureEngineering #linearRegression #modeling #probability #probabilities #statistics #stats #correctionRatio #ML #Pearson #bias #regressionRedress #distributions

@data @datadon 🧵

How to assess a statistical model?
How to choose between variables?

Pearson's #correlation is irrelevant if you suspect that the relationship is not a straight line.

If monotonic relationship:
"#Spearman’s rho is particularly useful for small samples where weak correlations are expected, as it can detect subtle monotonic trends." It is "widespread across disciplines where the measurement precision is not guaranteed".
"#Kendall’s Tau-b is less affected [than Spearman’s rho] by outliers in the data, making it a robust option for datasets with extreme values."
Ref: https://statisticseasily.com/kendall-tau-b-vs-spearman/

#normality #normalDistribution #modeling #dataDev #AIDev #ML #modelEvaluation #regression #modelling #dataLearning #featureEngineering #linearRegression #modeling #probability #probabilities #statistics #stats #correctionRatio #ML #Pearson #bias #regressionRedress #distributions

@[email protected] @[email protected] 🧵

How to assess a statistical model?
How to choose between variables?

Pearson's #correlation is irrelevant if you suspect that the relationship is not a straight line.

If monotonic relationship:
"#Spearman’s rho is particularly useful for small samples where weak correlations are expected, as it can detect subtle monotonic trends." It is "widespread across disciplines where the measurement precision is not guaranteed".
"#Kendall’s Tau-b is less affected [than Spearman’s rho] by outliers in the data, making it a robust option for datasets with extreme values."
Ref: https://statisticseasily.com/kendall-tau-b-vs-spearman/

#normality #normalDistribution #modeling #dataDev #AIDev #ML #modelEvaluation #regression #modelling #dataLearning #featureEngineering #linearRegression #modeling #probability #probabilities #statistics #stats #correctionRatio #ML #Pearson #bias #regressionRedress #distributions

@data @datadon 🧵

How to assess a statistical model?
How to choose between variables?

Pearson's #correlation is irrelevant if you suspect that the relationship is not a straight line.

If monotonic relationship:
"#Spearman’s rho is particularly useful for small samples where weak correlations are expected, as it can detect subtle monotonic trends." It is "widespread across disciplines where the measurement precision is not guaranteed".
"#Kendall’s Tau-b is less affected [than Spearman’s rho] by outliers in the data, making it a robust option for datasets with extreme values."
Ref: https://statisticseasily.com/kendall-tau-b-vs-spearman/

#normality #normalDistribution #modeling #dataDev #AIDev #ML #modelEvaluation #regression #modelling #dataLearning #featureEngineering #linearRegression #modeling #probability #probabilities #statistics #stats #correctionRatio #ML #Pearson #bias #regressionRedress #distributions

@data @datadon 🧵

How to assess a statistical model?
How to choose between variables?

Pearson's #correlation is irrelevant if you suspect that the relationship is not a straight line.

If monotonic relationship:
"#Spearman’s rho is particularly useful for small samples where weak correlations are expected, as it can detect subtle monotonic trends." It is "widespread across disciplines where the measurement precision is not guaranteed".
"#Kendall’s Tau-b is less affected [than Spearman’s rho] by outliers in the data, making it a robust option for datasets with extreme values."
Ref: https://statisticseasily.com/kendall-tau-b-vs-spearman/

#normality #normalDistribution #modeling #dataDev #AIDev #ML #modelEvaluation #regression #modelling #dataLearning #featureEngineering #linearRegression #modeling #probability #probabilities #statistics #stats #correctionRatio #ML #Pearson #bias #regressionRedress #distributions

9 Common interview questions for AI jobs - AI job seekers should be prepared to answer common interview ques... - https://cointelegraph.com/news/9-common-interview-questions-for-ai-jobs #unsupervisedlearning #interviewquestions #supervisedlearning #datapreprocessing #machinelearning #modelevaluation #non-technical #collaboration #technical #aijobs

I'm thinking about how to relate #ModelBuilding and #ModelEvaluation to #OpenScience. I'm not there yet, but anyone who wants to think along, feel free! Model evaluation, thinking about #validity and how #standardization and #generalisation interact, among others...

Figure on #standardisation vs #generalisation from https://doi.org/10.1111/j.1601-183X.2010.00628.x