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TrainingMetrics

Source code in docs_src/capo_frauddetector/types/training_metrics.py 
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class TrainingMetrics(TypedDict, closed=True):
    auc: NotRequired["capo_frauddetector.types.float.float"]
    """<p>The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across all possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect model has a score of 1.0.</p>"""
    metric_data_points: NotRequired[
        "capo_frauddetector.types.metric_data_points_list.metricDataPointsList"
    ]
    """<p>The data points details.</p>"""

auc instance-attribute 

auc: NotRequired[float]

The area under the curve. This summarizes true positive rate (TPR) and false positive rate (FPR) across all possible model score thresholds. A model with no predictive power has an AUC of 0.5, whereas a perfect model has a score of 1.0.

metric_data_points instance-attribute 

metric_data_points: NotRequired[metricDataPointsList]

The data points details.