snowflake.ml.modeling.metrics.precision_score¶

snowflake.ml.modeling.metrics.precision_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 precision.

The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative.

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’

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: 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:

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

Precision of the positive class in binary classification or weighted average of the precision of each class for the multiclass task.