Mindful Modeler
Christoph Molnar, Timo FreieslebenBetter machine learning by thinking like a statistician. About model interpretation, paying attention to data, and always staying critical.
| Platform |  | Pricing | Only free issues | Publishes | Weekly |
|---|---|---|---|---|---|
| Issues | 105 | Subscribers | Read | mindfulmodeler.substack.com |
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Latest Issues
Recent posts by this newsletter. Browse the email archive.
What is Mechanistic Interpretability and where did it come from?
When I heard about “Mechanistic Interpretability” (MI), I was confused. Behind the term, I found a motivation to interpret neural networks using methods like feature visualization. However, I was confused about whether it was somehow differ...
🐦⬛ Supervised Machine Learning for Science is published 🥳
We published our new book “Supervised Machine Learning for Science”. 🎉 🎉 🎉
How to implement permutation feature importance
Of all the methods for interpreting machine learning models, permutation feature importance is the easiest to implement. In the basic version, it’s just a few lines of code:
No learning without randomness
We call it noise, volatility, nuisance, variance, non-determinism, and uncertainty. We have to work hard to keep randomness under control: we try to keep our code reproducible with random seeds, we have to deal with missing values, and some...
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Authors
The writers behind this newsletter.
Writer and machine learning expert
I am a philosopher of machine learning and work on topics such as interpretable machine learning, robustness and causality.
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