Hi, Thanks for your post. However, the proposed solution will not work because the getFeatureID is needed to populated the weight matrix. So the proposed modifications to the code will result in not loading the model correctly and a wrong execution. The problem with the large memory requirement comes from loading the model itself with a double value to the weight matrix. To fix it, we need to code to remove the features that will not affect the classification (small weight) but still occupy memory. I would appreciate any fix to the large amount of heap needed to run the classification tasks. Regards
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