by Michele Laurelli
The harmonic mean of precision and recall, providing a single balanced metric.
F1 = 2 * (precision * recall) / (precision + recall). Useful for imbalanced datasets. Weighs precision and recall equally.
Binary classification evaluation
Imbalanced datasets
Medical diagnosis metrics
Metrics for classification: Precision is correct positives / predicted positives; Recall is correct positives / actual positives.
A supervised learning task where the goal is to predict discrete class labels for input data.
A table used to evaluate classification model performance by showing true vs predicted classes.