`R/metrics.R`

`metric_recall.Rd`

Computes the recall of the predictions with respect to the labels

metric_recall( ..., thresholds = NULL, top_k = NULL, class_id = NULL, name = NULL, dtype = NULL )

... | 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 |

top_k | (Optional) Unset by default. An int value specifying the top-k predictions to consider when calculating recall. |

class_id | (Optional) Integer class ID for which we want binary metrics.
This must be in the half-open interval |

name | (Optional) string name of the metric instance. |

dtype | (Optional) data type of the metric result. |

A (subclassed) `Metric`

instance that can be passed directly to
`compile(metrics = )`

, or used as a standalone object. See `?Metric`

for
example usage.

This metric creates two local variables, `true_positives`

and
`false_negatives`

, that are used to compute the recall. This value is
ultimately returned as `recall`

, an idempotent operation that simply divides
`true_positives`

by the sum of `true_positives`

and `false_negatives`

.

If `sample_weight`

is `NULL`

, weights default to 1. Use `sample_weight`

of 0
to mask values.

If `top_k`

is set, recall will be computed as how often on average a class
among the labels of a batch entry is in the top-k predictions.

If `class_id`

is specified, we calculate recall by considering only the
entries in the batch for which `class_id`

is in the label, and computing the
fraction of them for which `class_id`

is above the threshold and/or in the
top-k predictions.

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_at_precision()`

,
`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()`