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tfma.metrics.BinaryAccuracy

Calculates how often predictions match binary labels.

Inherits From: Metric

This metric computes the accuracy based on (TP + TN) / (TP + FP + TN + FN).

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

threshold (Optional) A float value in [0, 1]. The threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is true, below is false). If neither threshold nor top_k are set, the default is to calculate with threshold=0.5.
top_k (Optional) Used with a multi-class model to specify that the top-k values should be used to compute the confusion matrix. The net effect is that the non-top-k values are set to -inf and the matrix is then constructed from the average TP, FP, TN, FN across the classes. When top_k is used, metrics_specs.binarize settings must not be present. Only one of class_id or top_k should be configured.
class_id (Optional) Used with a multi-class model to specify which class to compute the confusion matrix for. When class_id is used, metrics_specs.binarize settings must not be present. Only one of class_id or top_k should be configured.
name (Optional) string name of the metric instance.

compute_confidence_interval Whether to compute confidence intervals for this metric.

Note that this may not completely remove the computational overhead involved in computing a given metric. This is only respected by the jackknife confidence interval method.

Methods

computations

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Creates computations associated with metric.

get_config

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Returns serializable config.

result

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Function for computing metric value from TP, TN, FP, FN values.