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Multi-class confusion matrix plot.

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. 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). Only one of either thresholds or num_thresholds should be used. If both are unset, then [0.0] will be assumed.
num_thresholds Number of thresholds to use. The thresholds will be evenly spaced between 0.0 and 1.0 and inclusive of the boundaries (i.e. to configure the thresholds to [0.0, 0.25, 0.5, 0.75, 1.0], the parameter should be set to 5). Only one of either thresholds or num_thresholds should be used.
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.


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Returns true if the metric does not depend on a model.