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
How linear regression works intuitively and how it leads to gradient descent
Assessment Of Snow Cover Dynamics And The Effects Of Environmental Drivers In High Mountain Ecosystems
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https://doi.org/10.1016/j.eiar.2025.107969 <-- shared paper
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#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
How to Train Machine Learning model withou ML Library with simple Python code a internal work ? then follow below link - it has video also
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
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/
#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
Redressing #Bias: "Correlation Constraints for Regression Models":
Treder et al (2021) https://doi.org/10.3389/fpsyt.2021.615754
"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
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.
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/
#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
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)
"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
Stephen Mansour from Misericordia University is presenting Taming Regression using APL at #Dyalog24. He's showing us how APL can be used to create functions for linear regression, with extensions to multiple and non-linear models.
Greg Cocks 
