[NEW]

[sourvad]

I would really like it if there was a probability measure of the decision similar to the "predict_proba_" method of scikit-learn models. This would enable model calibration and also help in calculating threshold based metrics (personally I have to get the ROC-AuC score)

I'm not familiar with the nooks and crannies of OPFython but my search yielded no results for features which may enable me to get probability scores instead of labels.

[gugarosa]

Hello sourvad! ! hope everything is going well with you!

Regarding the predicted probabilities, the standard Optimum-Path Forest classifier is not capable of doing so, as it performs the labeling process through a distance measure and directly assigns the label based on the prototype's label that conquers such a node.

Nevertheless, there is a recent paper in the literature, proposed by Fernandes et al., which introduces a probabilistic-based version of the OPF (https://ieeexplore.ieee.org/document/8329170). I still have plans on implementing it, yet I need to talk with the author and get a more insightful view of how the probabilistic version works.

If you have any problems or concerns, please let me know!

Best regards,
Gustavo.

[sourvad]

Hello Gustavo,

Thank you for letting me know that. I'm just beginning my journey in exploring Optimum-Path Forest classifier based classifiers and they are very interesting to me. I'll take a deeper look into how Optimum-Path Forest classifier and also how probabilistic-based Optimum-Path Forest classifier works.

Regards,
Sourab