matrics_calculator.MSE

Functions

mean_squared_error(y_true, y_pred)

Calculate the Mean Squared Error (MSE) between actual and predicted values.

Module Contents

matrics_calculator.MSE.mean_squared_error(y_true, y_pred)[source]

Calculate the Mean Squared Error (MSE) between actual and predicted values.

This function computes the average squared difference between the predicted values (y_pred) and the actual values (y_true). It is commonly used as a metric to evaluate regression models.

Parameters:

y_true (list or array-like) – The actual observed values.
y_pred (list or array-like) – The predicted values from the model.

Returns:

The Mean Squared Error (MSE) value, which is non-negative. A smaller value indicates that the predictions are closer to the actual values.

Return type:

float

Notes

MSE is defined as:: MSE = (1 / n) * sum((y_true - y_pred)²)

where n is the number of observations.

This function assumes that the input y_true and y_pred have the same length.

Examples

>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> mean_squared_error(y_true, y_pred)
0.375