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No, I think the features of this dataset are unique to it.

Can you update the article by holding out a portion of the training set and then using it as an unseen test set?

Otherwise it's impossible to make the comparisons at the end of the article to other results.

Yeah, was the model trained using train/test splits? otherwise, the model likely has been severely overfit.

I wonder how this performance would have compared to a simple random forest or MLP model.

I was also curious. Using an out-of-the-box weighted random forest, I got an f-score of ~.85 using a 75:25 stratified train-test split.