Bias metrics are crucial for evaluating the fairness and performance of machine learning models. Here's a breakdown of key concepts and tools:

Core Metrics

  • Accuracy
    Measures overall correctness:

    Accuracy_Metric
    Best for balanced datasets.

  • Precision
    Focuses on true positive rate:

    Precision_Recall
    Ideal for imbalanced classes.

  • Recall
    Highlights false negative detection:

    Recall_Metric
    Critical in medical diagnosis scenarios.

Advanced Tools

  • F1 Score
    Harmonizes precision and recall:

    F1_Score_Metric
    Useful for class imbalance.

  • ROC Curve & AUC
    Visualizes classification performance:

    ROC_AUC_Metric
    AUC represents area under the curve.

Practical Applications

  • Fairness Metrics
    Assess bias across groups (e.g., demographic parity, equal opportunity):
    Fairness_Metrics
    Important for ethical AI development.

For deeper insights into evaluation metrics, explore our Evaluation Metrics Tutorial.