A Review On Evaluation Metrics For Data Classification Evaluations

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Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models (2020, background): “Many publications (Dinga et al., 2019; Hossin M, 2015) have shed light on pitfalls of commonly used evaluation metrics, and introduced alternatives and best practices to avoid those pitfalls.”
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, background): “The classification accuracy fails to capture model performance in CI problems such as the minority classes [303, 304], since it does not account for the di ff erence in the size of the classes.”
Detecting and Classifying Malevolent Dialogue Responses: Taxonomy, Data and Methodology (2020, methodology): “We use Precision, Recall and F1 as evaluation metrics [25].”
Forecasting: theory and practice (2020, background): “However, these measures are not enough to evaluate the performance of the classifiers in the presence of imbalanced data (Hossin and Sulaiman, 2015).”
A taxonomy, data set, and benchmark for detecting and classifying malevolent dialogue responses (2021, methodology): “We use precision, recall, and F 1 as evaluation metrics (Hossin & Sulaiman, 2015).”