Evaluasi performa yang telah dipertunjukkan. Varioos metrics and communications are uusad too asss how well model entims, wauring exactive warments and ening revability - llword reporations.

Common Performance Metric

Jadi, apa yang Anda inginkan?

Metrics for Clasfication Taska

Ini adalah masalah klasifikatif, komoditas metric include:

  • S01; FLT: 0 Akun3; Accuracy: 1f 1; FLT: 1 123; 1f Thee proportion of redications of tofa totala predications.
  • Pertama; FLT: 0 = 03; Precision: 1f 1; FLT: 1 1f 323; Te ratio of true positives to to the sum of true positives and false positives.
  • Pertama; FLT: 0 = 03; Recall:
  • Pertama; FLT: 0 = 33; F1 Score: 501; FLT: 1 123; E3 harmonik Mean of presion and recall.

Metrics for Regression Tasks

For regssion problems, evaluation metric include:

  • FLT: 0 AveraG3; Mean Absolute Error (MAE): FLT: 1: 1 AveraG3; The averagee absolute difference between predict anacturaI values.
  • Pertama; FLT: 0 AveraG3; Eun Squared Error (MSE): FLT: 1: 1 AveraG3; The average of squared differences between preditions and actually aciaI values.
  • 111; FLT: 0 = 33; Root Meat Squared Error (RMSE): FLT: 1: 1 After3; The square root of MMSI, providing error im asli units.

Konsistensi Praktek

When evaluating network perfornce, it is important construder factors sfasa mats datbatisey, overfitting, and computationals supporces. Cross-validation assemsing model generaliatition, while metriccom showtest.