平均二乗誤差（MSE) is a statistical metric used to evaluate the accuracy of a model’s predictions by quantifying the difference between predicted values and the actual values observed in the data.
MSEを計算するための式は次のとおりです：
MSE = (1/n) * Σ(actual – predicted)²
ここで、 n is the number of observations, actual represents the true values, and predicted are the values generated by the model. The squared differences are used to ensure that positive and negative errors do not cancel each other out, emphasizing larger errors more than smaller ones.
MSEは広く使用されています 回帰分析 and 機械学習 to assess how well a model performs. A lower MSE value indicates better モデルのパフォーマンス, as it signifies that the predictions are closer to the actual values. Conversely, a higher MSE indicates larger errors and poorer model accuracy.
While MSE is a useful metric, it is important to note that it is sensitive to outliers due to the squaring of errors. Therefore, in cases where the data may contain outliers, other metrics like 平均絶対誤差 評価には（MAE）を考慮することもあります。
要約すると、平均二乗誤差は予測モデリングの基本的な概念です。 予測モデルの基本的な基盤として, providing a clear numeric value that reflects the quality of a model’s predictions.