Greg Cocks 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
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How to Train Machine Learning model withou ML Library with simple Python code a internal work ? then follow below link - it has video also
IT Consulting & IT Training · How to Train Machine Learning model withou ML Library with simple Python code a internal work ?Do you want to know How Machine Learning Work Internally With Respect to Training Please follow below video on YouTube and Following Presentation It cover how to use Python coding without ML Librar…
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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
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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/
LEARN STATISTICS EASILY · Kendall Tau-b vs Spearman: Which Correlation Coefficient Wins?Discover why Kendall Tau-b vs Spearman Correlation is crucial for your data analysis and which coefficient offers the most reliable results.
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Redressing #Bias: "Correlation Constraints for Regression Models":
Treder et al (2021) https://doi.org/10.3389/fpsyt.2021.615754
FrontiersFrontiers | Correlation Constraints for Regression Models: Controlling Bias in Brain Age PredictionIn neuroimaging, the difference between chronological age and predicted brain age, also known as brain age delta, has been proposed as a pathology marker lin...
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"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
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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
Towards Data Science · Predict Housing Price using Linear Regression in Python | Towards Data Science
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#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 
scikit-learn1.1. Linear ModelsThe following are a set of methods intended for regression in which the target value is expected to be a linear combination of the features. In mathematical notation, if\hat{y} is the predicted val...
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)
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/
teguhteja.id · Gradient Descent Optimization: Mastering Linear RegressionGradient Descent Optimization is key for efficient linear regression. Learn how to implement it effectively in Python.
#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/
teguhteja.id · Mastering Linear Regression with Python and Sklearn: A Step-by-Step GuideLinear regression Python sklearn guide: Master predictive modeling. Learn implementation, visualization, and practical applications.
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
ml-nn.euRoad Repair Cost PredictionMachine Learning & Neural Networks Blog
An easy guide to predict possible future quantities, by Mercy Kibet: https://www.influxdata.com/blog/guide-regression-analysis-time-series-data/#heading0
InfluxData · A Guide to Regression Analysis with Time Series Data | InfluxDataRegression analysis with time series data is a potent tool for understanding relationships between variables. #influxdb
Check out this exciting example showcasing the basics of artificial intelligence!
We used the basic linear regression model to predict outcomes.
If you found this interesting, drop a comment below!
Don't forget to like and follow for more cool content.
And the last question for ML people: Not so long ago, if I remember correctly, it wasn't possible to train a model to learn addition with real numbers outputting 100% accurate answers. I mean, it can be done even using linear regression, but the answers won't be 100% accurate for all cases.
Does this still hold true or am I wrong? Wouldn't it be overfitting?
One question for ML people: Machine learning was "taught" to me, back in the day as something that should only be used when 1) There is no exact #algorithm or tool to code what we want, and, 2) We don't need exact answers (ie, 100% accuracy).
Does this still hold true? My understanding is that it hasn't ever changed, or did it?
A #tutorial on building and #interpreting #linearRegression models for #inferential and #predictive #modeling with examples in #R.
dataalltheway.comData All The Way - Linear regression for inferential and predictive modelingA tutorial on building and interpreting linear regression models for inferential and predictive modeling with examples in R.