Analysis on Different Performance Metrics in Machine Learning Models

Abstract

Selecting an appropriate performance measure is necessary while measuring the performance of a machine learning model. Accuracy although often used is deceptive on class-imbalanced datasets. Precision states the positive number predicted correctly and is significant while fraud detection occurs. Recall or sensitivity states the true positives and is important while disease diagnosis is taking place. An equibalanced set of measures provides better model evaluation as well as better-informed decisions.
The F1-score, a harmonic mean of precision and recall, balances both aspects, making it ideal for imbalanced data. AUC-ROC assesses a model’s ability to differentiate between classes, offering a broader evaluation.
Choosing the right metric is critical for improving model performance, reducing errors, and ensuring alignment with real-world applications. Proper evaluation leads to more reliable, data-driven decisions.