snowflake.ml.modeling.metrics.accuracy_score¶

snowflake.ml.modeling.metrics.accuracy_score(*, df: DataFrame, y_true_col_names: Union[str, List[str]], y_pred_col_names: Union[str, List[str]], normalize: bool = True, sample_weight_col_name: Optional[str] = None) → float¶

Accuracy classification score.

In multilabel classification, this function computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in the y true columns.

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.

normalize: boolean, default=True

If False, return the number of correctly classified samples. Otherwise, return the fraction of correctly classified samples.

sample_weight_col_name: string, default=None

Column name representing sample weights.

Returns:

If normalize == True, return the fraction of correctly classified samples (float), else returns the number of correctly classified samples (int).

The best performance is 1 with normalize == True and the number of samples with normalize == False.