# allennlp.training.metrics.fbeta_measure#

## FBetaMeasure#

```FBetaMeasure(self, beta:float=1.0, average:str=None, labels:List[int]=None) -> None
```

Compute precision, recall, F-measure and support for each class.

The precision is the ratio `tp / (tp + fp)` where `tp` is the number of true positives and `fp` the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative.

The recall is the ratio `tp / (tp + fn)` where `tp` is the number of true positives and `fn` the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples.

The F-beta score can be interpreted as a weighted harmonic mean of the precision and recall, where an F-beta score reaches its best value at 1 and worst score at 0.

If we have precision and recall, the F-beta score is simply: `F-beta = (1 + beta ** 2) * precision * recall / (beta ** 2 * precision + recall)`

The F-beta score weights recall more than precision by a factor of `beta`. `beta == 1.0` means recall and precision are equally important.

The support is the number of occurrences of each class in `y_true`.

Parameters

• beta : `float`, optional (default = 1.0) The strength of recall versus precision in the F-score.

• average : string, [None (default), 'micro', 'macro'] If `None`, the scores for each class are returned. Otherwise, this

• determines the type of averaging performed on the data:

• `'micro'`: Calculate metrics globally by counting the total true positives, false negatives and false positives.

• `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.

• labels: list, optional The set of labels to include and their order if `average is None`. Labels present in the data can be excluded, for example to calculate a multi-class average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average.

### get_metric#

```FBetaMeasure.get_metric(self, reset:bool=False)
```

Returns

`A tuple of the following metrics based on the accumulated count statistics`: `precisions`: List[float] `recalls`: List[float] `f1-measures`: List[float]

If `self.average` is not `None`, you will get `float` instead of `List[float]`.

### reset#

```FBetaMeasure.reset(self) -> None
```

Reset any accumulators or internal state.