metric_precision
Computes the precision of the predictions with respect to the labels
Description
Computes the precision of the predictions with respect to the labels
Usage
metric_precision(
..., thresholds = NULL,
top_k = NULL,
class_id = NULL,
name = NULL,
dtype = NULL
)
Arguments
Arguments | Description |
---|---|
… | Passed on to the underlying metric. Used for forwards and backwards compatibility. |
thresholds | (Optional) A float value or a list 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) Unset by default. An int value specifying the top-k predictions to consider when calculating precision. |
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
The metric creates two local variables, true_positives
and false_positives
that are used to compute the precision. This value is ultimately returned as precision
, an idempotent operation that simply divides true_positives
by the sum of true_positives
and false_positives
. If sample_weight
is NULL
, weights default to 1. Use sample_weight
of 0 to mask values. If top_k
is set, we’ll calculate precision as how often on average a class among the top-k classes with the highest predicted values of a batch entry is correct and can be found in the label for that entry. If class_id
is specified, we calculate precision by considering only the entries in the batch for which class_id
is above the threshold and/or in the top-k highest 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_at_recall()
, 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()