snowflake.ml.modeling.metrics.explained_variance_score¶
- snowflake.ml.modeling.metrics.explained_variance_score(*, df: DataFrame, y_true_col_names: Union[str, List[str]], y_pred_col_names: Union[str, List[str]], sample_weight_col_name: Optional[str] = None, multioutput: Union[str, _SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]] = 'uniform_average', force_finite: bool = True) Union[float, ndarray[Any, dtype[float64]]] ¶
Explained variance regression score function.
Best possible score is 1.0, lower values are worse.
In the particular case when
y_true
is constant, the explained variance score is not finite: it is eitherNaN
(perfect predictions) or-Inf
(imperfect predictions). To prevent such non-finite numbers to pollute higher-level experiments such as a grid search cross-validation, by default these cases are replaced with 1.0 (perfect predictions) or 0.0 (imperfect predictions) respectively. Ifforce_finite
is set toFalse
, this score falls back on the original definition.- Note:
The Explained Variance score is similar to the
R^2 score
, with the notable difference that it does not account for systematic offsets in the prediction. Most often theR^2 score
should be preferred.- 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.
- sample_weight_col_name: string, default=None
Column name representing sample weights.
- multioutput: {‘raw_values’, ‘uniform_average’, ‘variance_weighted’} 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 scores in case of multioutput input.
- ‘uniform_average’:
Scores of all outputs are averaged with uniform weight.
- ‘variance_weighted’:
Scores of all outputs are averaged, weighted by the variances of each individual output.
- force_finite: boolean, default=True
Flag indicating if
NaN
and-Inf
scores resulting from constant data should be replaced with real numbers (1.0
if prediction is perfect,0.0
otherwise). Default isTrue
, a convenient setting for hyperparameters’ search procedures (e.g. grid search cross-validation).
- Returns:
- score: float or ndarray of floats
The explained variance or ndarray if ‘multioutput’ is ‘raw_values’.