snowflake.ml.modeling.metrics.d2_pinball_score¶

snowflake.ml.modeling.metrics.d2_pinball_score(*, df: DataFrame, y_true_col_names: str | list[str], y_pred_col_names: str | list[str], sample_weight_col_name: str | None = None, alpha: float = 0.5, multioutput: str | Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | _NestedSequence[complex | bytes | str] = 'uniform_average') → float | ndarray[tuple[Any, ...], dtype[float64]]¶

regression score function, fraction of pinball loss explained.

Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A model that always uses the empirical alpha-quantile of y_true as constant prediction, disregarding the input features, gets a score of 0.0.

Parameters:

• 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.

• sample_weight_col_name – string, default=None Column name representing sample weights.

• alpha – float, default=0.5 Slope of the pinball deviance. It determines the quantile level alpha for which the pinball deviance and also D2 are optimal. The default alpha=0.5 is equivalent to d2_absolute_error_score.

• multioutput –

  {‘raw_values’, ‘uniform_average’} or array-like of shape (n_outputs,), default=’uniform_average’ Defines aggregating of multiple output values. Array-like value defines weights used to average errors. ‘raw_values’:

  Returns a full set of errors in case of multioutput input.

  ’uniform_average’:

  Scores of all outputs are averaged with uniform weight.

Returns:

float or ndarray of floats

The score with a pinball deviance or ndarray of scores if multioutput=’raw_values’.

Return type:

score