All methods take 2 parameters, the actual results and the predicted results for a given task. deep_accuracy (aka Exact Match) and hamming_score is mainly for multilabel classification problems, and ...

f1 score is the harmonic mean that combines the precision and recall In classification problems, the "accuracy" metric might be irrelevant because of the large True Positive or False Negative.

Precision and recall, the components of the F1 score, provide a nuanced view of a model's behavior. Precision emphasizes the accuracy of positive predictions, while recall focuses on capturing all ...

We evaluate the model’s performance using metrics such as accuracy, precision, recall, F1 score, and AUC. Under five-fold cross-validation, the AUC reached 0.84, with all performance indicators ...