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The F1 score is a statistical measure used to evaluate the accuracy of a binary classification test by considering both precision and recall. It represents the harmonic mean of precision and recall, providing a balanced metric especially useful when the class distribution is uneven.
Precision and recall are fundamental metrics used to evaluate the performance of classification and information retrieval systems. Precision measures the accuracy of positive predictions, while recall measures the ability to identify all relevant instances.
AUC-ROC is a performance measurement for classification models at various threshold settings. It represents the area under the Receiver Operating Characteristic curve, summarizing the trade-off between true positive and false positive rates.