絶対誤差は、次の分野で使用される用語です statistics and データ分析 to quantify the accuracy of a prediction or measurement. It is defined as the absolute difference between the predicted value (or 観測値) と真の値（または実測値）との間の差を。数学的には、次のように表されます：
Absolute Error = | Predicted Value – Actual Value |
この指標は、どれだけ far off a prediction is from the actual result, regardless of the direction of the error (whether the prediction is above or below the actual value). As a result, Absolute Error is always a non-negative number.
AIや 機械学習, understanding Absolute Error helps in evaluating the performance of models. For instance, if you are building a regression model to predict housing prices, the Absolute Error will help you understand how close your model’s predictions are to the actual sale prices of the houses. By calculating the Absolute Error across all predictions, you can derive insights about the overall accuracy of the model and identify areas for improvement.
While Absolute Error is a useful metric, it does not provide a normalized view of error sizes, which is why it is often used in conjunction with other metrics, such as 平均絶対誤差 (MAE) or Root Mean Squared Error (RMSE), which average the Absolute Errors across multiple observations for a more comprehensive assessment of model performance.