Mean absolute error

machine_learning/loss_functions.py

@@ -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