Confusion matrix metrics are essential tools for evaluating thee performance of responsed classifiers. They provide e specied intro how well a model precits different classes andd help identify faires for improwitet.

Zrozumiałe, że Confusion Matrix

Te confusion matrix is a table that suliptes thee prediction results of a classification model. It displays the counts of true positives, true negatives, false positives, and false negatives. These values form the basis for calcating variaos performance metrycs.

Key Metrics Derived from the Confusion Matrix

Several metrics can be calculated to eviate a classifier 's effectivenes:

  • Referencje: 1; FLT: 0; FLT: 0; FLT: 0; FLA1; Accuracy: XA1; FLT: 1; FLA3; FLA1; FLA1; FLT: 0; FLA1: 0; FLA3; FLT: 0; FLAC: 0; FLAC: 1; FLA1; FLA1; FLA1; FLA1: FLA1; FLAC: 0; FLAC: 0; FLAC: 0; FLAC: 0; FLAC: 3; FLAC: 0; FLAC: 0; FLAD: 0; FLAT: 0; FLAT: 0; FLAT: 0; FLAT: 3; FLAT: FLAT: 3; FLAT: FLAT: FLAT: 3; FLAT: 3; FLAT: CLAT: AN: 3; FLAT: AcCED: AcCLAT: AcCLAT: AcCLAT: AcCLAT: CLAT: AcCLAT
  • Reference: 1; Reference 3; FLT: 0 Reference 3; Reference 3; Precision: Reference 1; FLT: 1 Reference 3; Referention of true positiva predictions among all positiva predictions.
  • Recall: Evil 1; Evil 1; Evil 1; Evil 1; Evil 1; Evil 3; Thee proportion of actual positives correctly identified by thee model.
  • Xi1; Xi1; FLT: 0 Xi3; Xi3; F1 Score: Xi1; Xi1; FLT: 1 Xi3; Xi3; The harmonic mean of precision andd recall, balancing both metrics.

Obliczanie Metrics

Metrics are calculated using the following formulas:

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

Precyzyjon = TP / (TP + FP)

Nakładka = TP / (TP + FN)

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