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Mean absolute error (#10927)

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* added mean absolute error to loss_functions.py

* added doctest to mean absolute error to loss_functions.py

* fixed long line in loss_functions.py

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fixed error in MAE

* Update machine_learning/loss_functions.py

Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com>

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com>
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Megan Payne 2023-10-26 07:31:47 +02:00 committed by GitHub
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machine_learning/loss_functions.py
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@ -261,6 +261,43 @@ def mean_squared_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
return np.mean(squared_errors)
def mean_absolute_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
"""
Calculates the Mean Absolute Error (MAE) between ground truth (observed)
and predicted values.
MAE measures the absolute difference between true values and predicted values.
Equation:
MAE = (1/n) * Σ(abs(y_true - y_pred))
Reference: https://en.wikipedia.org/wiki/Mean_absolute_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])
>>> np.isclose(mean_absolute_error(true_values, predicted_values), 0.16)
True
>>> 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])
>>> np.isclose(mean_absolute_error(true_values, predicted_values), 2.16)
False
>>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
>>> predicted_probs = np.array([0.3, 0.8, 0.9, 5.2])
>>> mean_absolute_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.")
return np.mean(abs(y_true - y_pred))
def mean_squared_logarithmic_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
"""
Calculate the mean squared logarithmic error (MSLE) between ground truth and