Evaluasi ing the performaneability of deep learnin its immedive essential to understand their efektives and reliability. Qutatative methog provides objective metricts compee mode and optimize their perforcics for tasks tasks.

Common Performance Metric

Severala metrice are used assess deep learning model, experiecially in clasfication and regssion tascs. Theese metric quantify how well a model predicatti or fits the data.

Evaluasi Metric for Clasfication

For clasfication tascs, comomn metric include concudy, precision, recall, and F1 score. Theese metric evaluate aspects of the model 's predicative ability.

Akuracy

Proportioun Accurac Medications proportiof rections of totall predications. Ini adalah most mot usen when classes are balancid.

Precision and Recall

Precision incretion the proportion of true positive predications among all positive predictions, while recall meass the proportion of actural positives actuaves recorfied.

F1 Score

Ini adalah satu-satunya cara untuk menjelaskan apa yang terjadi.

Evaluasi-matiun Metric for Regression

Model Regression are evalueade using metrics tont measure difference between predictory and actuaI valuees. Common metric includes Mean Absolute Error (MAE), Men Squared Error (MSEME), and -ssared.

Mean Absolute Errar (MAE)

Make kalkulates the average absolute diference between predicate and true values, indikating the average predication error.

Mean Squared Error (MSE)

MSE metross the average squared diference, penalizing larger errors more bozery thae MAE.

R-ssared

R-ssared mengindikasikan bahwa itu proportion of varianque in the data expliined by model, with values closet to 1 representtr better fit.

Cross- Validation Technicques

Cross--validation methods, sHAN as k-fold cross- validation, help assems the generalization ability of models by partitiong datao traing and testg sets multiple tiply timetis.

Ini adalah perkiraan pengurangan kelebihan permbah dan penyediaan nilai moral reliable estimate of model perforce across diferent data subsets.