What F1 Score Meaning, Applications & Example
Metric combining precision and recall for model evaluation.
What is F1 Score?
The F1 Score is a metric used to evaluate a model ’s accuracy by balancing precision and recall . It is the harmonic mean of precision (the proportion of true positive predictions among positive predictions) and recall (the proportion of true positive predictions among actual positives). The F1 Score ranges from 0 to 1, where 1 indicates perfect precision and recall.
Importance of F1 Score
The F1 Score is particularly useful in cases where there is an imbalance in the dataset, as it provides a more balanced measure of performance than accuracy alone.
Applications of F1 Score
- Spam Detection: Ensures both true positives and true negatives are considered for accurate spam filtering.
- Medical Diagnosis: Balances precision and recall to reduce the risk of false negatives in critical health predictions.
- Fraud Detection: Helps identify fraudulent transactions without missing too many legitimate ones.
Example of F1 Score
In a cancer detection model, if precision is 0.8 and recall is 0.6, the F1 Score would be approximately 0.7, representing a balance between the model’s ability to detect cancer (recall) and minimize false positives (precision).