Measupung presenti of a deep learnin g model is essential tevalue its perforcise. Divient metrice are uudipending on té type of problems, sf as clacification or relissoun. Understanding theteticrescins helps in beseconolks tole ths defoc.

Common Metric for Clasfication Models

For clascification tasks, predictions of all predications made. Howevek, othr metricres provides more detailed insileth, expericially with avaleus dateks.

Key Metrics for Evaluation

  • Pertama; FLT: 0 = 03; Precision: 1f; FLT: 1 1f 33; Te ratio of true positivos to te total previted positives.
  • FLT: 0 = Recall:
  • FLT: 0 F1 Score; F1: FLT:
  • FLT: 0 = 33; Konfusion Matrix:

Metric Kalkulating

Metrics are kalkulated using counts froms tre conpresion.

Metrics for Regression Models

Ini regssion tasks, metrics focus on the diference between predicted and actuaI values. Common metric includde mean squared error (MSE), men absolutte error (MAE), and R-squared.

Summary

Choosing the rightt metric depends on the problemm type and specic goals. Proper evaluation ensures the model performs well and meets te decree truciards.