Computes F-1 Score.
metrics_f1score( num_classes, average = NULL, threshold = NULL, name = "f1_score", dtype = tf$float32 )
| num_classes | Number of unique classes in the dataset. |
|---|---|
| average | Type of averaging to be performed on data. Acceptable values are NULL, micro, macro and weighted. Default value is NULL. - None: Scores for each class are returned - micro: True positivies, false positives and false negatives are computed globally. - macro: True positivies, false positives and - false negatives are computed for each class and their unweighted mean is returned. - weighted: Metrics are computed for each class and returns the mean weighted by the number of true instances in each class. |
| threshold | Elements of y_pred above threshold are considered to be 1, and the rest 0. If threshold is NULL, the argmax is converted to 1, and the rest 0. |
| name | (optional) String name of the metric instance. |
| dtype | (optional) Data type of the metric result. Defaults to `tf$float32`. |
F-1 Score: float
It is the harmonic mean of precision and recall. Output range is [0, 1]. Works for both multi-class and multi-label classification. F-1 = 2 * (precision * recall) / (precision + recall)
ValueError: If the `average` has values other than [NULL, micro, macro, weighted].
if (FALSE) { model = keras_model_sequential() %>% layer_dense(units = 10, input_shape = ncol(iris) - 1,activation = activation_lisht) %>% layer_dense(units = 3) model %>% compile(loss = 'categorical_crossentropy', optimizer = optimizer_radam(), metrics = metrics_f1score(3)) }