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主に【Interpretable Machine Learning】の第5項についての資料
原著
https://christophm.github.io/interpretable-ml-book/
日本語訳
https://hacarus.github.io/interpretable-ml-book-ja/
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主に【Interpretable Machine Learning】の第4項についての資料
原著
https://christophm.github.io/interpretable-ml-book/
日本語訳
https://hacarus.github.io/interpretable-ml-book-ja/
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Read through from c2 - c10 of the books, gathering knowledge asap.
#### Summary
Machine learning has great potential for improving products, processes and research. But computers usually do not …
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Eg, model-agnostic Shapley values.
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# Title
## Understanding SHAP for Interpretable Machine Learning: A Tutorial and Hands-on Workshop
# Responsible person(s)
Nicolás Nieto (n.nieto@fz-juelich.de) 1,2,
Federico Raimondo (f.raimo…
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* [christophM/interpretable-ml-book: Book about interpretable machine learning](https://github.com/christophM/interpretable-ml-book)
* [Interpretable Machine Learning](https://christophm.github.io/in…
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Hello, I have a pre-trained model for text sentiment polarity classification, with a structure roughly composed of RoBERTa+TextCNN. Can I use the Introspective Rationale Explainer to interpret its out…
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Hi, I can't work with "mldataset" package in python, those codes provided in new version don't work for "Jupyter notebook and google colab". can you provide alternative code for it? Whole chapter 2 wo…
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# Assessing Interpretable Models | Practical Cheminformatics
Understanding and comparing the rationale behind machine learning model predictions
[https://patwalters.github.io/practicalcheminformatic…
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### WHY
We need to reveal how the black-box model engine operates for user to understand and trust the systems (where they do not know the basis for the current outcomes and lack insight how it wor…