Formula:

```
b2 <- beta^2
f_beta_score <- (1 + b2) * (precision * recall) / (precision * b2 + recall)
```

This is the weighted harmonic mean of precision and recall.
Its output range is `[0, 1]`

. It works for both multi-class
and multi-label classification.

## Usage

```
metric_fbeta_score(
...,
average = NULL,
beta = 1,
threshold = NULL,
name = "fbeta_score",
dtype = NULL
)
```

## Arguments

- ...
For forward/backward compatability.

- average
Type of averaging to be performed across per-class results in the multi-class case. Acceptable values are

`NULL`

,`"micro"`

,`"macro"`

and`"weighted"`

. Defaults to`NULL`

. If`NULL`

, no averaging is performed and`result()`

will return the score for each class. If`"micro"`

, compute metrics globally by counting the total true positives, false negatives and false positives. If`"macro"`

, compute metrics for each label, and return their unweighted mean. This does not take label imbalance into account. If`"weighted"`

, compute metrics for each label, and return their average weighted by support (the number of true instances for each label). This alters`"macro"`

to account for label imbalance. It can result in an score that is not between precision and recall.- beta
Determines the weight of given to recall in the harmonic mean between precision and recall (see pseudocode equation above). Defaults to

`1`

.- threshold
Elements of

`y_pred`

greater than`threshold`

are converted to be 1, and the rest 0. If`threshold`

is`NULL`

, the argmax of`y_pred`

is converted to 1, and the rest to 0.- name
Optional. String name of the metric instance.

- dtype
Optional. Data type of the metric result.

## Value

a `Metric`

instance is returned. The `Metric`

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

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

for example usage.

## Examples

## See also

Other f score metrics: `metric_f1_score()`

Other metrics: `Metric()`

`custom_metric()`

`metric_auc()`

`metric_binary_accuracy()`

`metric_binary_crossentropy()`

`metric_binary_focal_crossentropy()`

`metric_binary_iou()`

`metric_categorical_accuracy()`

`metric_categorical_crossentropy()`

`metric_categorical_focal_crossentropy()`

`metric_categorical_hinge()`

`metric_cosine_similarity()`

`metric_f1_score()`

`metric_false_negatives()`

`metric_false_positives()`

`metric_hinge()`

`metric_huber()`

`metric_iou()`

`metric_kl_divergence()`

`metric_log_cosh()`

`metric_log_cosh_error()`

`metric_mean()`

`metric_mean_absolute_error()`

`metric_mean_absolute_percentage_error()`

`metric_mean_iou()`

`metric_mean_squared_error()`

`metric_mean_squared_logarithmic_error()`

`metric_mean_wrapper()`

`metric_one_hot_iou()`

`metric_one_hot_mean_iou()`

`metric_poisson()`

`metric_precision()`

`metric_precision_at_recall()`

`metric_r2_score()`

`metric_recall()`

`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()`