#linearregression — 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

"y=Xβ+ϵ and the interpretation of the coefficients"
Derek L. Sonderegger (2020), Statistical Methods: https://bookdown.org/dereksonderegger/571/

#probabilities #stats #statistics #ML #linearRegression #interpretability #RStats

Before diving into deep learning hype, remember the power of classic algorithms. Linear regression, decision trees, and thoughtful feature engineering still drive real‑world analytics and revenue. Master these fundamentals and your neural nets will perform better, faster, and cheaper. Curious how the basics outpace the buzz? Read on. #NeuralNetworks #LinearRegression #DecisionTrees #FeatureEngineering

🔗 https://aidailypost.com/news/master-fundamentals-before-neural-networks-core-algorithms-power

Before diving into deep learning hype, remember the power of classic algorithms. Linear regression, decision trees, and thoughtful feature engineering still drive real‑world analytics and revenue. Master these fundamentals and your neural nets will perform better, faster, and cheaper. Curious how the basics outpace the buzz? Read on. #NeuralNetworks #LinearRegression #DecisionTrees #FeatureEngineering

🔗 https://aidailypost.com/news/master-fundamentals-before-neural-networks-core-algorithms-power

How linear regression works intuitively and how it leads to gradient descent

https://briefer.cloud/blog/posts/least-squares/

#HackerNews #linearregression #gradientdescent #machinelearning #statistics #dataanalysis

Assessment Of Snow Cover Dynamics And The Effects Of Environmental Drivers In High Mountain Ecosystems
--
https://doi.org/10.1016/j.eiar.2025.107969 <-- shared paper
--
#GIS #spatial #mapping #remotesensing #earthobservation #snow #ice #snowcover #dynamics #climatechange #mountains #ecosystems #spatialanalysis #spatiotemporal #MODIS #model #modeling #extremeweather #water #hydrology #climate #zones #trendanalysis #linearregression #RandomForest #cryosphere

Is machine learning merely a form of curve-fitting?
#machinelearning #ai #curvefitting #linearregression #buzzwords

Is machine learning merely a form of curve-fitting?
#machinelearning #ai #curvefitting #linearregression #buzzwords

Is machine learning merely a form of curve-fitting?
#machinelearning #ai #curvefitting #linearregression #buzzwords

Is machine learning merely a form of curve-fitting?
#machinelearning #ai #curvefitting #linearregression #buzzwords

Is machine learning merely a form of curve-fitting?
#machinelearning #ai #curvefitting #linearregression #buzzwords

@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

#AI #interpretability vs #explainability 🧵

"The explanations themselves can be difficult to convey to nonexperts, such as end users and line-of-business teams" https://www.techtarget.com/searchenterpriseai/feature/Interpretability-vs-explainability-in-AI-and-machine-learning

#AIEthics #compliance #taxonomy #ethicalAI #AIEvaluation #linearRegression #trust #neuralNetworks #ML #governance #AIgovernance #safety #bias

@data @datadon 🧵

Redressing #Bias: "Correlation Constraints for Regression Models":
Treder et al (2021) https://doi.org/10.3389/fpsyt.2021.615754

#dataDev #linearRegression #modeling #probability #probabilities #statistics #stats #modelling #regression #correctionRatio #skLearn #scikitLearn #python #AIDev

@data @datadon 🧵

Redressing #Bias: "Correlation Constraints for Regression Models":
Treder et al (2021) https://doi.org/10.3389/fpsyt.2021.615754

#dataDev #linearRegression #modeling #probability #probabilities #statistics #stats #modelling #regression #correctionRatio #skLearn #scikitLearn #python #AIDev

@[email protected] @[email protected] 🧵

Redressing #Bias: "Correlation Constraints for Regression Models":
Treder et al (2021) https://doi.org/10.3389/fpsyt.2021.615754

#dataDev #linearRegression #modeling #probability #probabilities #statistics #stats #modelling #regression #correctionRatio #skLearn #scikitLearn #python #AIDev

@data @datadon 🧵

Redressing #Bias: "Correlation Constraints for Regression Models":
Treder et al (2021) https://doi.org/10.3389/fpsyt.2021.615754

#dataDev #linearRegression #modeling #probability #probabilities #statistics #stats #modelling #regression #correctionRatio #skLearn #scikitLearn #python #AIDev

@data @datadon 🧵

Redressing #Bias: "Correlation Constraints for Regression Models":
Treder et al (2021) https://doi.org/10.3389/fpsyt.2021.615754

#dataDev #linearRegression #modeling #probability #probabilities #statistics #stats #modelling #regression #correctionRatio #skLearn #scikitLearn #python #AIDev

"In real life, we weigh the anticipated consequences of the decisions that we are about to make. That approach is much more rational than limiting the percentage of making the error of one kind in an artificial (null hypothesis) setting or using a measure of evidence for each model as the weight."
Longford (2005) http://www.stat.columbia.edu/~gelman/stuff_for_blog/longford.pdf

#modeling #nullHypothesis #probability #probabilities #pValues #statistics #stats #statisticalLiteracy #bias #inference #modelling #regression #linearRegression

"Feature importance helps in understanding which features contribute most to the prediction"

A few lines with #sklearn: https://mljourney.com/sklearn-linear-regression-feature-importance/

#interpretability #explainability #AIethics #compliance #taxonomy #ethicalAI #AIevaluation #linearRegression #featureEngineering

"Feature importance helps in understanding which features contribute most to the prediction"

A few lines with #sklearn: https://mljourney.com/sklearn-linear-regression-feature-importance/

#interpretability #explainability #AIethics #compliance #taxonomy #ethicalAI #AIevaluation #linearRegression #featureEngineering

"Feature importance helps in understanding which features contribute most to the prediction"

A few lines with #sklearn: https://mljourney.com/sklearn-linear-regression-feature-importance/

#interpretability #explainability #AIethics #compliance #taxonomy #ethicalAI #AIevaluation #linearRegression #featureEngineering

"Feature importance helps in understanding which features contribute most to the prediction"

A few lines with #sklearn: https://mljourney.com/sklearn-linear-regression-feature-importance/

#interpretability #explainability #AIethics #compliance #taxonomy #ethicalAI #AIevaluation #linearRegression #featureEngineering

"Feature importance helps in understanding which features contribute most to the prediction"

A few lines with #sklearn: https://mljourney.com/sklearn-linear-regression-feature-importance/

#interpretability #explainability #AIethics #compliance #taxonomy #ethicalAI #AIevaluation #linearRegression #featureEngineering

@datadon

#Lasso #LinearRegression "is useful in some contexts due to its tendency to prefer solutions with fewer non-zero coefficients, effectively reducing the number of features upon which the given solution is dependent"

https://scikit-learn.org/stable/modules/linear_model.html#lasso 🧵

#dataDev #AIDev #ML #sklearn #python #interpretability

@datadon

#Lasso #LinearRegression "is useful in some contexts due to its tendency to prefer solutions with fewer non-zero coefficients, effectively reducing the number of features upon which the given solution is dependent"

https://scikit-learn.org/stable/modules/linear_model.html#lasso 🧵

#dataDev #AIDev #ML #sklearn #python #interpretability

@[email protected]

#Lasso #LinearRegression "is useful in some contexts due to its tendency to prefer solutions with fewer non-zero coefficients, effectively reducing the number of features upon which the given solution is dependent"

https://scikit-learn.org/stable/modules/linear_model.html#lasso 🧵

#dataDev #AIDev #ML #sklearn #python #interpretability

@datadon

#Lasso #LinearRegression "is useful in some contexts due to its tendency to prefer solutions with fewer non-zero coefficients, effectively reducing the number of features upon which the given solution is dependent"

https://scikit-learn.org/stable/modules/linear_model.html#lasso 🧵

#dataDev #AIDev #ML #sklearn #python #interpretability

@datadon

#Lasso #LinearRegression "is useful in some contexts due to its tendency to prefer solutions with fewer non-zero coefficients, effectively reducing the number of features upon which the given solution is dependent"

https://scikit-learn.org/stable/modules/linear_model.html#lasso 🧵

#dataDev #AIDev #ML #sklearn #python #interpretability

For the next few months, Dr. Andrej-Nikolai Spiess (https://openalex.org/works?page=1&filter=authorships.author.id%3Aa5027948408&sort=publication_year%3Adesc) will be a guest in my working group.

We are working on a paper where we show that 29 % of papers in top journals like Science, Nature & PNAS were skewed by a single influential data point! Time to rethink our reliance on p-values and explore alternative measures like #dfstat. #reproducibilitycrisis #linearregression #rstats

Moreover, we will work on #qPCR related software like PCRedux (https://joss.theoj.org/papers/10.21105/joss.04407)

#JOSS

For the next few months, Dr. Andrej-Nikolai Spiess (https://openalex.org/works?page=1&filter=authorships.author.id%3Aa5027948408&sort=publication_year%3Adesc) will be a guest in my working group.

We are working on a paper where we show that 29 % of papers in top journals like Science, Nature & PNAS were skewed by a single influential data point! Time to rethink our reliance on p-values and explore alternative measures like #dfstat. #reproducibilitycrisis #linearregression #rstats

Moreover, we will work on #qPCR related software like PCRedux (https://joss.theoj.org/papers/10.21105/joss.04407)

#JOSS

For the next few months, Dr. Andrej-Nikolai Spiess (https://openalex.org/works?page=1&filter=authorships.author.id%3Aa5027948408&sort=publication_year%3Adesc) will be a guest in my working group.

We are working on a paper where we show that 29 % of papers in top journals like Science, Nature & PNAS were skewed by a single influential data point! Time to rethink our reliance on p-values and explore alternative measures like #dfstat. #reproducibilitycrisis #linearregression #rstats

Moreover, we will work on #qPCR related software like PCRedux (https://joss.theoj.org/papers/10.21105/joss.04407)

#JOSS

For the next few months, Dr. Andrej-Nikolai Spiess (https://openalex.org/works?page=1&filter=authorships.author.id%3Aa5027948408&sort=publication_year%3Adesc) will be a guest in my working group.

We are working on a paper where we show that 29 % of papers in top journals like Science, Nature & PNAS were skewed by a single influential data point! Time to rethink our reliance on p-values and explore alternative measures like #dfstat. #reproducibilitycrisis #linearregression #rstats

Moreover, we will work on #qPCR related software like PCRedux (https://joss.theoj.org/papers/10.21105/joss.04407)

#JOSS

For the next few months, Dr. Andrej-Nikolai Spiess (https://openalex.org/works?page=1&filter=authorships.author.id%3Aa5027948408&sort=publication_year%3Adesc) will be a guest in my working group.

We are working on a paper where we show that 29 % of papers in top journals like Science, Nature & PNAS were skewed by a single influential data point! Time to rethink our reliance on p-values and explore alternative measures like #dfstat. #reproducibilitycrisis #linearregression #rstats

Moreover, we will work on #qPCR related software like PCRedux (https://joss.theoj.org/papers/10.21105/joss.04407)

#JOSS

@datadon

"The following sections discuss several state-of-the-art interpretable and explainable #ML methods. The selection of works does not comprise an exhaustive survey of the literature. Instead, it is meant to illustrate the commonest properties and inductive biases behind interpretable models and [black-box] explanation methods using concrete instances."
https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.1493#widm1493-sec-0010-title 🧵

#interpretability #explainability #aiethics #compliance #taxonomy #ethicalai #aievaluation #linearRegression

Discover how Gradient Descent Optimization enhances linear regression. This guide covers implementation in Python, key concepts, and practical tips. #MachineLearning #DataScience #GradientDescent #LinearRegression #Python

https://teguhteja.id/gradient-descent-optimization-in-linear-regression/

Comparing Linear and Logistic Regression.

Discussion on an entry level data science interview question.

(by Devesh Rajadhyax | Nov, 2022 | Towards Data Science)

https://towardsdatascience.com/comparing-linear-and-logistic-regression-11a3e1812212

#statistics #research #data #datascience #dataanalytics #linreg #logreg #linearregression #logisticregression #dataresearch #sociology #psychology @sociology