snowflake.ml.modeling.metrics.fbeta_scoreΒΆ
- snowflake.ml.modeling.metrics.fbeta_score(*, df: DataFrame, y_true_col_names: Union[str, List[str]], y_pred_col_names: Union[str, List[str]], beta: float, 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]]] ΒΆ
Compute the F-beta score.
The F-beta score is the weighted harmonic mean of precision and recall, reaching its optimal value at 1 and its worst value at 0.
The beta parameter determines the weight of recall in the combined score.
beta < 1
lends more weight to precision, whilebeta > 1
favors recall (beta -> 0
considers only precision,beta -> +inf
only recall).- Args:
df: Input dataframe. y_true_col_names: Column name(s) representing actual values. y_pred_col_names: Column name(s) representing predicted values. beta: Determines the weight of recall in the combined score. labels: The set of labels to include when
average != 'binary'
, andtheir 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]
andaverage != 'binary'
will report scores for that label only.- average: {βmicroβ, βmacroβ, βsamplesβ, βweightedβ, βbinaryβ} or None, default=βbinaryβ
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 βmacroβ to account for label imbalance; it can result in an F-score that is not between precision and recall.
'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: βwarnβ, 0 or 1, default=βwarnβ
Sets the value to return when there is a zero division, i.e. when all predictions and labels are negative. If set to βwarnβ, this acts as 0, but warnings are also raised.
- pos_label: The class to report if
- Returns:
- fbeta_score - float (if average is not None) or array of float, shape = [n_unique_labels]
F-beta score of the positive class in binary classification or weighted average of the F-beta score of each class for the multiclass task.