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About Interpretable Machine Learning
This document discusses interpretability in machine learning and identifies 10 technical challenges in interpretable machine learning. It provides background on the importance of interpretability to avoid issues from black box models and outlines fundamental principles. The challenges discussed range from optimizing sparse logical models like decision trees to incorporating physics constraints to characterizing sets of good models. Addressing these challenges can help develop machine learning models that are inherently interpretable.
Detailed Information
| Author: | ['Tomasz Mikołajczyk'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 80 |
| Language: | English |
| Format: | |
| Price: | FREE |
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