Facial Recognition
Machine Learning
A balanced measure combining precision and recall into a single metric. Provides a balanced assessment of model performance.
Detailed Explanation
The F1 Score is a metric that combines precision and recall into a single value by calculating their harmonic mean. It is especially useful when seeking a balance between false positives and false negatives. The score ranges from 0 to 1, with higher values indicating better model performance, making it ideal for evaluating classification models, particularly in imbalanced datasets.
Use Cases
- •Evaluating a spam detection model's accuracy in identifying genuine emails versus false positives.
Related Terms
Other terms in the Machine Learning category