Evaluating thee performance of machine learning models is essential to understand their effectiveness. In conceped learning, metrics such as precisacy, precision, and recall are common ly used to measure how well a model predicts outcomes.

Přesnost

Accuracy measures the proportion of correct predictions out of all predictions made. It is calculated by diviming thee number of correct predictions by they thotal number of predictions.

While preciacy is useful, it can be misleading in imbalanced datasets where one class dominates. In such cases, their metrics providee better insights into model performance.

Precision and Recall

Precision indicates the proportion of true positive predictions among all positive predictions made by thee model. It reflects thee model 's ability to o avoid false positives.

Recall, also know n as sensitivity, measures the proportion of actual positives correctly identified by thee model. It shows how well thee model detects positive cases.

Calculating thee metrics

These metrics are derived from thae confusion matrix, which summazes true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN).

Diploma:

  • CLAS1; CLAS1; CLAS3; CLAS3; CLAS3; CLAS1; CLAS1; CLAS1; CLAS3; CLAS3; (TP + TN) / (TP + FP + TN + FN)
  • CLANE1; CLANE1; CLANE1; CLANE3; CLANE3; CLANE3; CLANE1; CLANE1; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3O3; CLANE3O3; CLANE3O4: CLANE1; CLANE1; CLANE1O4: CLANE3; CLANE3; CLANE3; CLANE3; CLANE3O4; CLANEKATIFORMATION: CLANE3; CLANEKTERIAMONISI; CLANEIFORMATI3O4; CLANIVA)
  • CLANE1; CLANE1; FLT: 0 CLANE3; CLANE3; CLANE3; CLANE1; CLANE1; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE1; CLANE1; CLANE1; CLANE3; CLANE3; CLANE3; CLANE3; TP / (TP + FN)