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Computes F-Beta score.

Source: R/metrics.R
metric_fbeta_score.Rd

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

metric <- metric_fbeta_score(beta = 2.0, threshold = 0.5)
y_true <- rbind(c(1, 1, 1),
                c(1, 0, 0),
                c(1, 1, 0))
y_pred <- rbind(c(0.2, 0.6, 0.7),
                c(0.2, 0.6, 0.6),
                c(0.6, 0.8, 0.0))
metric$update_state(y_true, y_pred)
metric$result()

## tf.Tensor([0.3846154  0.90909094 0.8333332 ], shape=(3), dtype=float32)

Returns

F-Beta Score: float.

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()