In conceded classification problems, evaluating thee performance of a model is essential. Metrics like precision, recall, and F1 score providee intenghts into how well thee model predicts different classes. Understanding how to calculate these metrics helps in selecting and tuning models effectively.

Precision

Precision measures thee proportion of positive predictions that are correct. It is calculated as th te number of true positives divided by that sum of true positives and false positives.

Te formula for precision is:

CLANE1; CLANE1; CLANE3; CLANE3; CLANE3; CLANE3O3; CLANE3O3 = True Positives / (True Positives + False Positives) CLANE1; CLANE1; CLANE3O3; CLANE3O3;

Recall

Recall, also know in as sensitivity, measures the proportion of actual positives that are correctly identified. It is calculated as thos number of true positives divided by sum of true positives and false negatives.

Te formula for recall is:

CLAS1; CLAS1; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3s = True Positives / (True Positives + False Negatives) CLAS1; CLAS1; CLAS3; CLAS33s: 1 CLAS3; CLAS33c; CLAS3c;

F1 Score

Te F1 score combine concision and recall into a single metric by calculating their harmonic mean. It provides a balance d measure, especially whey n class distribution is uneven.

Te formula for F1 score is:

CLAS1; CLAS1; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3e = 2 * (Precision * Recall) / (CLAS3O3) CLAS1; CLAS1; CLAS3O3;

Summary

  • Precision indicates thee prescacy of positive predictions.
  • Recall measures thee ability to identifify all positive instances.
  • Te F1 score balances precision and recall into a single metric.