F1 Score: Balancing Precision and Recall in AI Evaluation

The F1 Score is a widely used metric in AI model evaluation, providing a balanced measure of precision and recall. It is particularly useful in scenarios where both false positives and false negatives carry significant consequences, such as fraud detection, medical diagnosis, and security systems. The F1 Score is calculated as the harmonic mean of precision and recall, ranging from 0 to 1, with higher values indicating better performance. Different variants of the F1 Score, including Macro F1, Micro F1, Weighted F1, and Fβ Score, provide more nuanced insights in multi-class classification and imbalanced datasets. The choice of variant depends on the specific requirements of the task, such as equal class importance or precision being more important than recall. However, the F1 Score has limitations, including not accounting for class distribution and not providing insights into model confidence. Galileo's Evaluation Intelligence Platform offers a holistic assessment of AI models in imbalanced datasets, granular error tracking, and confidence-based insights.