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[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], 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.

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.

• 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:

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.

Return type:

loss