Computes best recall where precision is >= specified value
metric_recall_at_precision(
...,
precision,
num_thresholds = 200L,
class_id = NULL,
name = NULL,
dtype = NULL
)
A (subclassed) Metric
instance that can be passed directly to
compile(metrics = )
, or used as a standalone object. See ?Metric
for
example usage.
Passed on to the underlying metric. Used for forwards and backwards compatibility.
A scalar value in range [0, 1]
.
(Optional) Defaults to 200. The number of thresholds to use for matching the given precision.
(Optional) Integer class ID for which we want binary metrics.
This must be in the half-open interval [0, num_classes)
, where
num_classes
is the last dimension of predictions.
(Optional) string name of the metric instance.
(Optional) data type of the metric result.
For a given score-label-distribution the required precision might not be achievable, in this case 0.0 is returned as recall.
This metric creates four local variables, true_positives
, true_negatives
,
false_positives
and false_negatives
that are used to compute the recall
at the given precision. The threshold for the given precision value is
computed and used to evaluate the corresponding recall.
If sample_weight
is NULL
, weights default to 1. Use sample_weight
of 0
to mask values.
If class_id
is specified, we calculate precision by considering only the
entries in the batch for which class_id
is above the threshold predictions,
and computing the fraction of them for which class_id
is indeed a correct
label.
Other metrics:
custom_metric()
,
metric_accuracy()
,
metric_auc()
,
metric_binary_accuracy()
,
metric_binary_crossentropy()
,
metric_categorical_accuracy()
,
metric_categorical_crossentropy()
,
metric_categorical_hinge()
,
metric_cosine_similarity()
,
metric_false_negatives()
,
metric_false_positives()
,
metric_hinge()
,
metric_kullback_leibler_divergence()
,
metric_logcosh_error()
,
metric_mean_absolute_error()
,
metric_mean_absolute_percentage_error()
,
metric_mean_iou()
,
metric_mean_relative_error()
,
metric_mean_squared_error()
,
metric_mean_squared_logarithmic_error()
,
metric_mean_tensor()
,
metric_mean_wrapper()
,
metric_mean()
,
metric_poisson()
,
metric_precision_at_recall()
,
metric_precision()
,
metric_recall()
,
metric_root_mean_squared_error()
,
metric_sensitivity_at_specificity()
,
metric_sparse_categorical_accuracy()
,
metric_sparse_categorical_crossentropy()
,
metric_sparse_top_k_categorical_accuracy()
,
metric_specificity_at_sensitivity()
,
metric_squared_hinge()
,
metric_sum()
,
metric_top_k_categorical_accuracy()
,
metric_true_negatives()
,
metric_true_positives()