Multi-class confusion matrix metrics at thresholds.

Inherits From: Metric

Computes weighted example counts for all combinations of actual / (top) predicted classes.

The inputs are assumed to contain a single positive label per example (i.e. only one class can be true at a time) while the predictions are assumed to sum to 1.0.

thresholds Optional thresholds, defaults to 0.5 if not specified. If the top prediction is less than a threshold then the associated example will be assumed to have no prediction associated with it (the predicted_class_id will be set to NO_PREDICTED_CLASS_ID).
name Metric name.

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.



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Creates computations associated with metric.


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Returns serializable config.