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()