Precision, recall, and F1-score are important metrics used to evaluate te performance of NLP classification models. They help in commercing how well a model predicts different classes, especially in imbalanced datasets.

Understanding Precision

Precision measures thee proportion of true positive predictions among all positive predictions made by te model. It indicates how many of thee predicted positive cases are actually positive.

Understanding Recall

Recall, also know in as sensitivity, measures the proportion of actual positive cases that are correctly identified by thee model. It reflects thee model 's ability to detect positive instances.

Calculating thee F1-Score

Te F1-score is the harmonic mean of precision and recall. It provides a single metric that balances both, especially useful when thee class distribution is uneven.

Example Calculation

Předpověď a model predicts 80 positive cases, of which 60 are correct. Thee total actual positive cases are 70. Thee calculations are as follows:

  • CLANE1; CLANE1; CLANE1; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3O1; CLANE1; CLANE1; CLANE1; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3c = 0, 75
  • CLANE1; CLANE1; FLT: 0 CLANE3; CLANE3; CLANE3; CLANE1; CLANE1; CLANE1; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE1; CLANE1; CLANE1; CLANE1; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3c; CLANE3CLANE3; CLANE3CLANE3; CLANE3CLANE3; CLANE3CLANE3CLANE.1.0; CLANE11; CLANE11111CLANE11CLAVI.1; CLANE3CLAVIDE1; CLAVIDE3; CLANER111CLANER1CLAND: 1; CLAVICLAVICLAVICLAVICLAVICLAVICLAVICLAVI@@
  • CLAS1; CLAS1; FLT: 0 CLAS3; CLAS3; F1- Score: CLAS1; CLAS1; FLAS1; FLAS3; FLAS3; 2 * (0, 75 * 0, 857) / (0, 75 + 0, 857) CLAS3; CLAS3; FLAS3; CLAS3; 2 * (0, 75 * 0, 857) / (0, 75 + 0, 857) CLAS0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S0S@@