Function mean_squared_error

machine_learning/loss_functions.py:333–363

Calculate the mean squared error (MSE) between ground truth and predicted values. MSE measures the squared difference between true values and predicted values, and it serves as a measure of accuracy for regression models. MSE = (1/n) * Σ(y_true - y_pred)^2 Reference: https://

(y_true: np.ndarray, y_pred: np.ndarray)

def mean_squared_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
    """
    Calculate the mean squared error (MSE) between ground truth and predicted values.

    MSE measures the squared difference between true values and predicted values, and it
    serves as a measure of accuracy for regression models.

    MSE = (1/n) * Σ(y_true - y_pred)^2

    Reference: https://en.wikipedia.org/wiki/Mean_squared_error

    Parameters:
    - y_true: The true values (ground truth)
    - y_pred: The predicted values

    >>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
    >>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
    >>> bool(np.isclose(mean_squared_error(true_values, predicted_values), 0.028))
    True
    >>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
    >>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
    >>> mean_squared_error(true_labels, predicted_probs)
    Traceback (most recent call last):
    ...
    ValueError: Input arrays must have the same length.
    """
    if len(y_true) != len(y_pred):
        raise ValueError("Input arrays must have the same length.")

    squared_errors = (y_true - y_pred) ** 2
    return np.mean(squared_errors)


def mean_absolute_error(y_true: np.ndarray, y_pred: np.ndarray) -> float: