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tfma.metrics.Recall

Computes the recall of the predictions with respect to the labels.

Inherits From: Metric

The metric uses true positives and false negatives to compute recall by dividing the true positives by the sum of true positives and false negatives.

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

thresholds (Optional) A float value or a python list/tuple of float threshold values in [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is true, below is false). One metric value is generated for each threshold value. If neither thresholds nor top_k are set, the default is to calculate precision with thresholds=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.