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This is an quick motivation for the article "Please Version Data": win-vector.com/2024/09/09/please-version-data/

I'll be returning to BARUG Tuesday, October 15, 2024 to talk on using Stan to "learn to rank" (infer user preferences from observed selections and rejections). Links: www.meetup.com/r-users/events/303488652/?eventOrigin=group_upcoming_events github.com/WinVector/Examples/blob/main/rank/LearningToRank.pdf

The strange out of sample behavior of linear regression when the number of training examples is equal to the number of model parameters. Article link: win-vector.com/2024/04/03/the-m-n-machine-learning-anomaly/

This is a video introduction to my tutorial article on how more data is better in training machine learning models. win-vector.com/2024/04/02/how-data-quantity-drives-model-quality github.com/WinVector/Examples/blob/main/TrainingSetSize/TrainingSetSize.ipynb

Quick example of how to render the same Jupyter notebook for different settings of control parameters. Please see here for more details: win-vector.com/2024/02/06/use-jupyter-notebooks-inside-for-loops/

I discuss the key steps of how to "put Jupyter data science projects intro production" using the wvpy package. github.com/WinVector/wvpy/tree/main/examples/declare_variables

This is a history of recent AI to help people responsible for decision quality have some terminology and orientation to evaluate AI systems and claims. Slides and links can be found here: github.com/WinVector/Examples/tree/main/decision_intel_ai . A note on AI hallucination can be found here: win-vector.com/2023/11/13/yet-another-chatgpt-winge/

Animation from Dr. Nina Zumel's Win Vector LLC Data Science article: Unrolling the ROC Curve win-vector.com/2023/10/19/unrolling-the-roc-curve/ See (with an animation!) how the ROC curve, threshold plot, and decision areas all relate. #datascience #pydata #abtesting

From Dr. Nina Zumel neat new Win Vector LLC Data Science article: Unrolling the ROC Curve win-vector.com/2023/10/19/unrolling-the-roc-curve/ See (with an animation!) how the ROC curve, threshold plot, and decision areas all relate. #datascience #pydata #abtesting

Humming bird upper fountain

This video is an introduction to the idea of partial pooling: combining data from multiple sources to get lower expected error! For the actual details of how to derive and use the methods in Python, please see here: github.com/WinVector/Examples/blob/main/PartialPooling/PartialPooling.ipynb .

This is an introduction to my article on how much data is needed in data science projects. The full article can be found here: win-vector.com/2023/03/18/how-much-data-do-you-need/ .

This lecture uses ideas and notation from this article: win-vector.com/2023/03/12/thinking-about-linear-regression/ . And, I am trying to have a bit of fun with the setup.

Humming bird girl who hides in our quince.

Parameterized Juypter Notebooks

Working with Jupyter Notebooks using wvpy

How to install a Python environment for data science (Mac)

Data Science Bite: What is Statistics

AB Testing

What is Machine Learning

Kelly Thorp Betting

The Game of Infinity Questions

Bilingual Data Science

The Dual Numbers

Relative RMSE

R squared

Against Accuracy

Generalization Error

PAC Learning and VC Dimension