Definition
The false positive rate test measures the ratio of false positives to the total number of actual negatives, calculated as FP / (FP + TN). This metric indicates how often the model incorrectly predicts the positive class when the true class is negative.Taxonomy
- Task types: Tabular classification, text classification.
- Availability: and .
The false positive rate is only available for binary classification tasks.
Why it matters
- False positive rate is crucial for understanding the model’s tendency to make incorrect positive predictions.
- It’s particularly important in applications where false positives are costly, such as medical diagnosis, fraud detection, or spam filtering.
- Lower false positive rates indicate better model performance, with 0 representing no false positives.
- This metric complements precision and recall by focusing specifically on the negative class performance.
Required columns
To compute this metric, your dataset must contain the following columns:- Predictions: The predicted class labels from your binary classification model
- Ground truths: The actual/true class labels
Test configuration examples
If you are writing atests.json, here are a few valid configurations for the false positive rate test:
Related
- Precision test - Measure positive prediction accuracy.
- Recall test - Measure ability to find all positive instances.
- ROC AUC test - Area under the receiver operating characteristic curve.
- Accuracy test - Overall classification correctness.
- Aggregate metrics - Overview of all available metrics.