Konfusion matrix metrice are essential tools for evaluating thate perforceof convised clasfififer.

Memahami bahwa Confusion Matrix

Ini membingungkan sebuah tabloid summarzes dan predition results of a clacification model. Ini bukan subject the of true positives, true netitives, false positives, and false neetimev.

Key Metrics Derived fromm the Confusion Matrix

Severala metrics can be kalkulated to evaluate a clasfieir 's efectivenestes s:

  • S01; FLT: 0 Akun3; Accuracy: 1f 1; FLT: 1 123; At33; Thee proportion of mengoreksi of all predications.
  • FLT: 0 = 03; Precision: 501; FLT: 1 1f 323; Thee proportion of true positives predications among all positive predications.
  • Pertama; FLT: 0 = 03; Recall: 1.1; FLT: 1 After3; THe proportion of acturaI positives reviely identifed the model.
  • FLT: 0 F1 Score; F1: FLT:

Metric Kalkulating

Metrics are kalkulated using te following formula.

Accuracy = (TP + TN) / (TP + TN + FP + FN)

Precision = TP / (TP + FP)

Recall = TP / (TP + FN)

F1 Score = 2 * (Precision * Recall) / (Precision + Recall)