Computes the precision of the predictions with respect to the labels
metric_precision(
...,
thresholds = NULL,
top_k = NULL,
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.
(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.
(Optional) Unset by default. An int value specifying the top-k predictions to consider when calculating 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.
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.
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()