Max error

• Captura el peor caso posible.

• Se computa como:

  \text{Max error}(y, \hat{y}) = \max(|y_i - \hat{y}_i|) = \max(|\hat{e}|)

from sklearn.metrics import max_error

y_true = [3, 2, 7, 1]
y_pred = [4, 2, 7, 1]

max_error(
    # -------------------------------------------------------------------------
    # Ground truth (correct) target values.
    y_true=y_true,
    # -------------------------------------------------------------------------
    # Estimated target values.
    y_pred=y_pred,
)

1