It seems like you're facing an issue with the dimensionality mismatch when applying one-hot encoding to new data that has fewer categories compared to the original data used to train your Neural Network (NN). One possible solution is to ensure that the encoding of the new data matches the same dimensions as the original data.
Here's a suggestion to handle this situation:
1. Determine the unique categories of the original data that was used for training. Let's call this set of unique categories `original_categories`.
2. When you encounter new data, one-hot encode the field as usual. However, instead of using the one-hot encoding directly, ensure that the resulting one-hot encoded vector has the same dimensions as the original data.
- Create a list of unique categories in the new data. Let's call this set of unique categories `new_categories`.
- Compare `new_categories` with `original_categories` to identify any missing categories.
- Add the missing categories to `new_categories` and sort them in the same order as `original_categories`.
- Perform one-hot encoding on the field using the updated `new_categories` to ensure the dimensions match.
By aligning the categories and dimensions of the one-hot encoding for both the training data and new data, you can ensure consistency when running the new data through the Neural Network.
Additionally, it's worth noting that if you encounter categories in the new data that were not present in the original training data, the Neural Network may not have learned how to handle these categories effectively. In such cases, it's important to consider how to handle these unseen categories appropriately.
I hope this suggestion helps resolve the dimensionality mismatch issue. Let me know if you have any further questions!
