Comments (2)
Hi @neomatrix369 ,
Our example/tutorial notebooks contain a special section for the explainability. If you have a look here https://github.com/dreamquark-ai/tabnet/blob/develop/census_example.ipynb , there is a section subtitled Local explainability and masks
where we try to explain, for each input data line and for each step in the architecture, what are the mask values as well as the final explain_matrix
.
Of course, if you have suggestions on how to better show these results or how to make them clearer we'd love to hear your feedback!
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Thank you for sharing the link, I will give it a whirl!
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Related Issues (20)
- Loss goes to -inf HOT 1
- The mask tensor M in script tab_network.py needs to be transformed to realize the objective stated in the paper: "γ is a relaxation parameter – when γ = 1, a feature is enforced to be used only at one decision step".
- Current version on conda-forge is 4.0 while 4.1 is already released HOT 8
- Minimal working example for TabNetRegressor/Classifier HOT 4
- Transfer learning, capability to change structure of model HOT 1
- Generate Embeddings for Tabular Data HOT 1
- TabNet overfits (help wanted, not a bug) HOT 9
- TabNetRegressor vs other networks HOT 1
- spike in memory when training ends HOT 8
- Severe overfitting HOT 18
- OOM problem when I search hyperparameters with Tabnet HOT 3
- Support for complex-valued datasets HOT 4
- Different classification variables in the test set and train set HOT 1
- Struggling to get model to fit - Help Wanted HOT 7
- Optimizing TabNet for Disease Classification with Continuous Audio Features HOT 1
- Interpreting Sparsity on Global Importance HOT 5
- ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() HOT 1
- Validation loss HOT 1
- Lightweight Fine-tunning or few-shot learning for limited labeled data HOT 1
- Maybe `drop_last` should be set as False in default? HOT 1
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