snowflake.ml.modeling.metrics.mean_squared_error¶

snowflake.ml.modeling.metrics.mean_squared_error(*, 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', squared: bool = True) → Union[float, ndarray[Any, dtype[float64]]]¶

Mean squared error regression loss.

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’} 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’:

Errors of all outputs are averaged with uniform weight.

squared: boolean, default=True

If True returns MSE value, if False returns RMSE value.

Returns:

loss: float or ndarray of floats

A non-negative floating point value (the best value is 0.0), or an array of floating point values, one for each individual target.