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Mindful Modeler

Christoph Molnar, Timo Freiesleben

Better machine learning by thinking like a statistician. About model interpretation, paying attention to data, and always staying critical.

Platform
Substack
PricingOnly free issuesPublishesWeekly
Issues105SubscribersRead 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...

8 days ago
10
0

🐦‍⬛ Supervised Machine Learning for Science is published 🥳

We published our new book “Supervised Machine Learning for Science”. 🎉 🎉 🎉

16 days ago
12
1

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:

22 days ago
13
3

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...

a month ago
20
6

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Authors

The writers behind this newsletter.

  • Christoph Molnar

    Writer and machine learning expert

  • Timo Freiesleben

    I am a philosopher of machine learning and work on topics such as interpretable machine learning, robustness and causality.

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