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Once created sample models for CNNs (#32) and RNNs (#36), select from the training set of the competition a sample term that contains enough positive and negative examples*, train them with both, and then compare their results as in #9, plotting ROC curves, comparing the AUC, etc.
This analysis should be done in a notebook in order to show the usage of the developed capabilities of the library.
proteins that have the selected term assigned and others that don't, enough should be at least > 100 proteins, more will be better but they should fit in your RAM when training the set otherwise you will start to swap and things could go really slow.
The text was updated successfully, but these errors were encountered:
Once created sample models for CNNs (#32) and RNNs (#36), select from the training set of the competition a sample term that contains enough positive and negative examples*, train them with both, and then compare their results as in #9, plotting ROC curves, comparing the AUC, etc.
This analysis should be done in a notebook in order to show the usage of the developed capabilities of the library.
The text was updated successfully, but these errors were encountered: