ML.EVAL.CLASSIFICATION.RECALL¶
Returns the recall classification score.
Syntax¶
ML.EVAL.CLASSIFICATION.RECALL(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)
Arguments¶
| Name | Type | Default | Description |
|---|---|---|---|
| y_true | object | DataFrame or array object of ground-truth target values. | |
| y_pred | object | DataFrame or array object of predicted target values. | |
| labels | object | None | Optional array of class labels to include in the score, in the order they should appear. |
| pos_label | Any | 1 | Class to report as the positive class for binary metrics. |
| average | Any | "binary" | How to average across classes. One of 'binary' (positive class only), 'micro', 'macro', 'weighted', 'samples', or leave blank for per-class scores. |
| sample_weight | Any | None | Optional DataFrame or array object of per-sample weights. Omit for uniform weights. |
| zero_division | Any | "warn" | Value to return when the metric has a zero denominator. One of 'warn', 0, or 1. |
Examples¶
Examples coming soon
Working Excel formula examples for this function are not yet written.
See also¶
- ML.EVAL.CLASSIFICATION.ACCURACY
- ML.EVAL.CLASSIFICATION.AVERAGE_PRECISION
- ML.EVAL.CLASSIFICATION.BALANCED_ACCURACY
- ML.EVAL.CLASSIFICATION.BRIER_SCORE_LOSS
- ML.EVAL.CLASSIFICATION.D2_LOG_LOSS_SCORE
- ML.EVAL.CLASSIFICATION.F1
- ML.EVAL.CLASSIFICATION.JACCARD
- ML.EVAL.CLASSIFICATION.LOG_LOSS
- ML.EVAL.CLASSIFICATION.MATTHEWS_CORRCOEF
- ML.EVAL.CLASSIFICATION.PRECISION
- ML.EVAL.CLASSIFICATION.ROC_AUC
- ML.EVAL.CLASSIFICATION.TOP_K_ACCURACY