bigframes.ml.metrics.mean_squared_error#

bigframes.ml.metrics.mean_squared_error(y_true: DataFrame | Series, y_pred: DataFrame | Series) → float[source]#

Mean squared error regression loss.

Examples:

>>> import bigframes.pandas as bpd
>>> import bigframes.ml.metrics

>>> y_true = bpd.DataFrame([3, -0.5, 2, 7])
>>> y_pred = bpd.DataFrame([2.5, 0.0, 2, 8])
>>> mse = bigframes.ml.metrics.mean_squared_error(y_true, y_pred)
>>> mse
np.float64(0.375)

Parameters:

• y_true (Series or DataFrame of shape (n_samples,)) Ground truth (correct) target values.

• y_pred (Series or DataFrame of shape (n_samples,)) Estimated target values.

Returns:

Mean squared error.

Return type:

float