sklearn.metrics.max_error

sklearn.metrics.max_error(y_true, y_pred)

max_error metric calculates the maximum residual error.

Read more in the User Guide.

Parameters:

y_true : array-like of shape = (n_samples)

Ground truth (correct) target values.

y_pred : array-like of shape = (n_samples)

Estimated target values.

Returns:

max_error : float

A positive floating point value (the best value is 0.0).

Examples

>>> from sklearn.metrics import max_error
>>> y_true = [3, 2, 7, 1]
>>> y_pred = [4, 2, 7, 1]
>>> max_error(y_true, y_pred)
1