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Having as input Protein.go_terms(), predictions and a probability_threshold (what we consider positive and negative in our predictions) we should be able to assess our:
precision or PPV (positive predictive value)
recall or TPR (true positive rate)
For further descriptions of what these terms mean the Wikipedia article for ROC curves is detailed enough. Indeed, all the methods and the competition papers show these kinds of curves so we should be able to plot this for our models.
This functionality should be available in manas-cafa5.metrics.
The text was updated successfully, but these errors were encountered:
Having as input
Protein.go_terms()
, predictions and aprobability_threshold
(what we consider positive and negative in our predictions) we should be able to assess our:precision
orPPV
(positive predictive value)recall
orTPR
(true positive rate)For further descriptions of what these terms mean the Wikipedia article for ROC curves is detailed enough. Indeed, all the methods and the competition papers show these kinds of curves so we should be able to plot this for our models.
This functionality should be available in
manas-cafa5.metrics
.The text was updated successfully, but these errors were encountered: