metric_precision_at_recall: Computes best precision where recall is >= specified value

In keras: R Interface to 'Keras'

Computes best precision where recall is >= specified value

Description

Computes best precision where recall is >= specified value

Usage

metric_precision_at_recall(
  ...,
  recall,
  num_thresholds = 200L,
  class_id = NULL,
  name = NULL,
  dtype = NULL
)

Arguments

...	Passed on to the underlying metric. Used for forwards and backwards compatibility.
recall	A scalar value in range ⁠[0, 1]⁠.
num_thresholds	(Optional) Defaults to 200. The number of thresholds to use for matching the given recall.
class_id	(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.
name	(Optional) string name of the metric instance.
dtype	(Optional) data type of the metric result.

Details

This metric creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the precision at the given recall. The threshold for the given recall value is computed and used to evaluate the corresponding precision.

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.

Value

A (subclassed) Metric instance that can be passed directly to compile(metrics = ), or used as a standalone object. See ?Metric for example usage.

See Also

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(), metric_recall_at_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()

keras documentation built on Aug. 16, 2023, 1:07 a.m.