RSE - Residual Standard Error
\[\text{RSE}(y, f_i) = \sqrt{\frac{\sum_{i=1}^{n}(y_i - f_i)^2}{n-p-1}}\]
Latex equation code:
\text{RSE}(y, f_i) = \sqrt{\frac{\sum_{i=1}^{n}(y_i - f_i)^2}{n-p-1}}
Residual Standard Error (RSE): Best possible score is 0.0, smaller value is better. Range = [0, +inf)
The Residual Standard Error (RSE) is a metric used to evaluate the goodness of fit of a regression model. It measures the average distance between the
observed values and the predicted values.
Example to use RSE 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.residual_standard_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.RSE(multi_output="raw_values"))