Computes F-Beta score.
metric_fbetascore(
num_classes,
average = NULL,
beta = 1,
threshold = NULL,
name = "fbeta_score",
dtype = tf$float32,
...
)Number of unique classes in the dataset.
Type of averaging to be performed on data. Acceptable values are None, micro, macro and weighted. Default value is NULL. micro, macro and weighted. Default value is NULL. - None: Scores for each class are returned - micro: True positivies, false positives and false negatives are computed globally. - macro: True positivies, false positives and - false negatives are computed for each class and their unweighted mean is returned. - weighted: Metrics are computed for each class and returns the mean weighted by the number of true instances in each class.-
Determines the weight of precision and recall in harmonic mean. Determines the weight given to the precision and recall. Default value is 1.
Elements of y_pred greater than threshold are converted to be 1, and the rest 0. If threshold is None, the argmax is converted to 1, and the rest 0.
(optional) String name of the metric instance.
(optional) Data type of the metric result. Defaults to `tf$float32`.
additional parameters to pass
F-Beta Score: float
ValueError: If the `average` has values other than [NULL, micro, macro, weighted].
It is the weighted harmonic mean of precision and recall. Output range is [0, 1]. Works for both multi-class and multi-label classification. F-Beta = (1 + beta^2) * (prec * recall) / ((beta^2 * prec) + recall)