Better machine learning by thinking like a statistician. About model interpretation, paying attention to data, and always staying critical.
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Issues | 105 | Subscribers | Read | mindfulmodeler.substack.com |
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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...
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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:
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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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