#linearregression — Public Fediverse posts

The weathers, they are a-changing.
We can make sense of pattern evolution.
I am starting a thread about Europe's new #climate conditions. 🧵

(You may read stories in the "ALT" descriptions.)

#trend #heatwave #dataViz #chart #loess #linearRegression #statistics #France #football #WorldCup #Maignan #history #coloniality #weather #summer #Europe #climate #climateChange #globalWarming

The weathers, they are a-changing.
We can make sense of pattern evolution.
I am starting a thread about Europe's new #climate conditions. 🧵

(You may read stories in the "ALT" descriptions.)

#trend #heatwave #dataViz #chart #loess #linearRegression #statistics #France #football #WorldCup #Maignan #history #coloniality #weather #summer #Europe #climate #climateChange #globalWarming

During the second heatwave of 2026, temperature in France was in the band of trend variability.

(You may read stories in the "ALT" description.) Hat tip to @denismongin.bsky.social‬

#football #chart #dataViz #loess #linearRegression #statistics #climate #climateChange #globalWarming #France #trend #heatwave #WorldCup #Maignan #history #coloniality

During the second heatwave of 2026, temperature in France was in the band of trend variability.

(You may read stories in the "ALT" description.) Hat tip to @denismongin.bsky.social‬

#football #chart #dataViz #loess #linearRegression #statistics #climate #climateChange #globalWarming #France #trend #heatwave #WorldCup #Maignan #history #coloniality

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

"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

This same idea scales up in modern AI systems:
learn from data → predict.

Linear regression isn’t about complexity.
It’s about building intuition — and realizing you can understand how intelligent systems learn.

#sameidea #linearregression #intelligentsystems #intelligentsystem #scaleup

What actually powers LinearRegression under the hood? This piece digs into the hidden engine behind it and why that internal design matters for your models.

Read More: https://zalt.me/blog/2026/01/hidden-linear-engine

#LinearRegression #MachineLearning #MLModels #SoftwareDesign

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

Unlock the secrets of Linear Regression Machine Learning! A comprehensive guide for beginners. Dive into predictive modeling and data analysis. #MachineLearning #LinearRegression #DataScience

https://teguhteja.id/linear-regression-machine-learning-guide/?utm_source=mastodon&utm_medium=jetpack_social

Unlock the secrets of Linear Regression Machine Learning! A comprehensive guide for beginners. Dive into predictive modeling and data analysis. #MachineLearning #LinearRegression #DataScience

https://teguhteja.id/linear-regression-machine-learning-guide/?utm_source=mastodon&utm_medium=jetpack_social

S1 EP1 T1 - Most basic machine learning example #machinelearning #linearregression #python #jupyternotebook #jupyter #datascience #alogrithim #statistics #coding #codingforbeginners #deeplearning #mathematics #dataengineering #learncoding

S1 EP1 T1 - Most basic machine learning example #machinelearning #linearregression #python #jupyternotebook #jupyter #datascience #alogrithim #statistics #coding #codingforbeginners #deeplearning #mathematics #dataengineering #learncoding

Understanding Linear Regression https://hackaday.com/2025/05/08/understanding-linear-regression/ #linearregression #MachineLearning #math

Understanding Linear Regression https://hackaday.com/2025/05/08/understanding-linear-regression/ #linearregression #MachineLearning #math

Understanding Linear Regression - Although [Vitor Fróis] is explaining linear regression because it relates to machi... - https://hackaday.com/2025/05/08/understanding-linear-regression/ #linearregression #machinelearning #math

Understanding Linear Regression - Although [Vitor Fróis] is explaining linear regression because it relates to machi... - https://hackaday.com/2025/05/08/understanding-linear-regression/ #linearregression #machinelearning #math

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

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

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

@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

#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

#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

"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

"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 Selection in Python; a script ready to use: https://johfischer.com/2021/08/06/correlation-based-feature-selection-in-python-from-scratch/

#interpretability #featureSelection #python #probability #probabilities #bigData #classification #linearRegression #regression #Schusterbauer #inference #AIDev

Feature Selection in Python; a script ready to use: https://johfischer.com/2021/08/06/correlation-based-feature-selection-in-python-from-scratch/

#interpretability #featureSelection #python #probability #probabilities #bigData #classification #linearRegression #regression #Schusterbauer #inference #AIDev

The Coding Train dude is precious. This is the Math teacher I wish I had for every grade I was taught math. https://www.youtube.com/watch?v=szXbuO3bVRk #math #mathematics #linearregression

The Coding Train dude is precious. This is the Math teacher I wish I had for every grade I was taught math. https://www.youtube.com/watch?v=szXbuO3bVRk #math #mathematics #linearregression

In Elisa Yao's newest article, she breaks down the process of implementing Linear Regression in Python using a simple dataset known as “Boston Housing”, step by step.

#LinearRegression #Python

https://towardsdatascience.com/predict-housing-price-using-linear-regression-in-python-bfc0fcfff640

"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

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

@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/

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/

#LinearRegression #Python #Sklearn
Dive into predictive modeling with our comprehensive guide on linear regression using Python and sklearn. Learn step-by-step implementation, result interpretation, and data visualization techniques. Perfect for beginners

https://teguhteja.id/mastering-linear-regression-with-python-and-sklearn-a-step-by-step-guide/

#LinearRegression #Python #Sklearn
Dive into predictive modeling with our comprehensive guide on linear regression using Python and sklearn. Learn step-by-step implementation, result interpretation, and data visualization techniques. Perfect for beginners

https://teguhteja.id/mastering-linear-regression-with-python-and-sklearn-a-step-by-step-guide/

Road Repair Cost Prediction

This project aims to predict the total cost of road repairs using a Linear Regression model. The process involves data preprocessing, training a machine learning model, evaluating its performance, and visualizing the results.

Project page:
https://ml-nn.eu/project3.html

Project data sets + Jupyter notebook:
https://buymeacoffee.com/calinsandu/e/258050

#Learntoday #MachineLearning #NeuralNetworks #Python #AI #Programming #LinearRegression #BuyMeACoffee

Road Repair Cost Prediction

This project aims to predict the total cost of road repairs using a Linear Regression model. The process involves data preprocessing, training a machine learning model, evaluating its performance, and visualizing the results.

Project page:
https://ml-nn.eu/project3.html

Project data sets + Jupyter notebook:
https://buymeacoffee.com/calinsandu/e/258050

#Learntoday #MachineLearning #NeuralNetworks #Python #AI #Programming #LinearRegression #BuyMeACoffee

An easy guide to predict possible future quantities, by Mercy Kibet: https://www.influxdata.com/blog/guide-regression-analysis-time-series-data/#heading0

#timeSeries #data #inference #linearRegression #dataScience #futures #money #trends #Python