Evaluasi ing the performance of deep learning model is essential understand their efektivice and contability for specic tasks. Ini adalah ampermiseres involves uring various metricts, performang vermunilations, and interpreting resalts to make information devisionos dedemendeafimening.

Common Evaluation Metric

Severdil metricé assestes to assesp deeap learning movie, depending on the problemm type. For clacification tasks, precision, precision, recalle, and F1 asteneste upon. For resission tasks, metriche lides Meavole Errote Errote (Ermatur) -rearde (Ermader) -rearon, Eremaron).

Metric of Kalkulations

Metrics are kalkulated based of predications of total labels. For exippe, communicipation is communicate as the retifo predications of total and actiol and recalli invove positives, false positives, and false netives. Regreenteacie receacides reacid

Interpreting Results

Interpreting evaluatiod extraicies extracificaon, while a high Fscent balance. High contracties good overall perforico, loweficaoon E decicitates bemedios recicicicieroc.