平均絶対誤差

平均絶対誤差（MAE）

Mean 絶対誤差 (MAE) is a statistical measure used to assess the accuracy of a model’s predictions. It quantifies the average absolute difference between the actual values and the values predicted by the model. Unlike some other error metrics, MAE treats all errors equally, regardless of their direction (i.e., whether predictions are above or below the actual values).

MAEを計算するには、次の手順に従います：

1. 各観測値について、実際の値から予測値を引いて誤差を求めます。
2. 各誤差の絶対値を取り、負の差異を避けます。
3. すべての絶対誤差を合計します。
4. 合計を観測値の数で割ります。

MAEの式は次のように数学的に表すことができます：

MAE = (1/n) * Σ |Actual_i – Predicted_i|

where n is the number of observations, 実際の値_i is the actual value, and Predictedi_i は予測値です。

MAE is a widely used metric in fields such as 機械学習 and forecasting because it is easy to understand and interpret. A lower MAE value indicates a better fit of the model to the data, meaning the predictions are closer to the actual values. However, it is important to note that MAE does not provide information about the direction of errors (whether predictions are overestimates or underestimates), which may be relevant in certain applications.