snowflake.ml.modeling.metrics.recall_score¶

snowflake.ml.modeling.metrics.recall_score(*, df: DataFrame, y_true_col_names: Union[str, List[str]], y_pred_col_names: Union[str, List[str]], labels: Optional[Union[_SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]] = None, pos_label: Union[str, int] = 1, average: Optional[str] = 'binary', sample_weight_col_name: Optional[str] = None, zero_division: Union[str, int] = 'warn') → Union[float, ndarray[Any, dtype[float64]]]¶

Compute the recall.

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 best value is 1 and the worst value is 0.

Args:

df: Input dataframe. y_true_col_names: Column name(s) representing actual values. y_pred_col_names: Column name(s) representing predicted values. labels: The set of labels to include when average != 'binary', and

their order if average is None. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in the y true and y pred columns are used in sorted order.

pos_label: The class to report if average='binary' and the data is

binary. If the data are multiclass or multilabel, this will be ignored; setting labels=[pos_label] and average != 'binary' will report scores for that label only.

average: {‘micro’, ‘macro’, ‘samples’, ‘weighted’, ‘binary’} or None, default=’binary’

This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data: 'binary'

Only report results for the class specified by pos_label. This is applicable only if targets (y true, y pred) are binary.

'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.

'weighted'

Calculate metrics for each label, and find 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 F-score that is not between precision and recall. Weighted recall is equal to accuracy.

'samples'

Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from func`accuracy_score`).

sample_weight_col_name: Column name representing sample weights. zero_division: “warn”, 0 or 1, default=”warn”

Sets the value to return when there is a zero division. If set to “warn”, this acts as 0, but warnings are also raised.

Returns:

recall - float (if average is not None) or array of float of shape (n_unique_labels,)

Recall of the positive class in binary classification or weighted average of the recall of each class for the multiclass task.