SE - Squared Error

\[\text{SE}(y, f_i) = \frac{1}{n}\sum_{i=1}^{n}(y_i - f_i)^2\]

Latex equation code:

\text{SE}(y, f_i) = \frac{1}{n}\sum_{i=1}^{n}(y_i - f_i)^2

• Best possible score is 0.0, smaller value is better. Range = [0, +inf)

• Note: Computes the squared error between two numbers, or for element between a pair of list, tuple or numpy arrays.

• The Squared Error (SE) is a metric used to evaluate the accuracy of a regression model by measuring the average of the squared differences between the

predicted and actual values.

Example to use SE metric:

from numpy import array
from permetrics.regression import RegressionMetric

## For 1-D array
y_true = array([3, -0.5, 2, 7])
y_pred = array([2.5, 0.0, 2, 8])

evaluator = RegressionMetric(y_true, y_pred)
print(evaluator.single_squared_error())

## For > 1-D array
y_true = array([[0.5, 1], [-1, 1], [7, -6]])
y_pred = array([[0, 2], [-1, 2], [8, -5]])

evaluator = RegressionMetric(y_true, y_pred)
print(evaluator.SE())