snowflake.ml.modeling.metrics.f1_score¶

snowflake.ml.modeling.metrics.f1_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 F1 score, also known as balanced F-score or F-measure.

The F1 score can be interpreted as a harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal. The formula for the F1 score is:

F1 = 2 * (precision * recall) / (precision + recall)

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In the multi-class and multi-label case, this is the average of the F1 score of each class with weighting depending on the average parameter.

Args:

df: snowpark.DataFrame

Input dataframe.

y_true_col_names: string or list of strings

Column name(s) representing actual values.

y_pred_col_names: string or list of strings

Column name(s) representing predicted values.

labels: list of labels, default=None

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: string or integer, default=1

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.

'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: string, default=None

Column name representing sample weights.

zero_division: “warn”, 0 or 1, default=”warn”

Sets the value to return when there is a zero division, i.e. when all predictions and labels are negative. If set to “warn”, this acts as 0, but warnings are also raised.

Returns:

f1_score - float or array of float, shape = [n_unique_labels]

F1 score of the positive class in binary classification or weighted average of the F1 scores of each class for the multiclass task.