Comments (1)
Hello,
This is not a bug, and even if you could get rid of an error during training and inference by setting your embedding sizes to a large value that would not solve your problem, only silence it.
You can't hope for any model to predict something meaningful to an integer encoded new category. You will simply generate a random representation and make predictions out of noise: garbage in, garbage out.
You need to decide on your pipeline what happens with "new" or "unknown" category. There is a vast amount of options you can pick: replace any new category by the most frequent one, create a "rare values" category during your training and set new categories to this value and many more. That's something you need to deal on your own pipeline and is not taken care of by the library as it's important to understand and have control on this.
from tabnet.
Related Issues (20)
- 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
- 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
- Incompatiblity of current round() method with pytorch tensors when performing early stopping HOT 1
- Retraining a saved model on different dataset HOT 3
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from tabnet.