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Official Implementation of TMLR's paper: "TabCBM: Concept-based Interpretable Neural Networks for Tabular Data"

Home Page: https://openreview.net/pdf?id=TIsrnWpjQ0

License: MIT License

Python 100.00%
artificial-intelligence concept-based-explanations concept-based-models concepts explainability explainable-ai explainable-artificial-intelligence interpretable-deep-learning neural-networks tabular

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tabcbm's Issues

What are the models that needed in the TabCBM

Hello!
I am trying to use TabCBM for my own tabular datasets which are provided in CSV file. In the model provided, it required some required models such as:
feature_to_concept_model
concept_to_feature_model

So, What does these parts means? It is not referred to it in the paper with details. For example, assume that we have tabular data for train with 1000 rows and 7 columns which are features and 1000 elements vector with binary elements. How can we use TabCBM to train a model for test data?

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