L2損失、一般に 平均二乗誤差 (MSE), is a popular 損失関数 機械学習で使用される and 統計的モデリング to measure the accuracy of a model’s predictions. It quantifies the difference between the actual values and the values predicted by the model.
数学的には、L2損失は各予測値と対応する実際の値との差の二乗の平均を取ることで計算されます。式は次の通りです：
L2 Loss = (1/n) * Σ(actual – predicted)²
where n is the number of observations, actual is the actual value, and predicted は予測値です。
The key characteristic of L2 Loss is that it heavily penalizes larger errors due to the squaring of the differences. This property makes it sensitive to outliers, which can significantly affect the 広く使われている value. As a result, L2 Loss is often used in regression tasks where the goal is to minimize the error between predicted and actual values, leading to more accurate models.
While L2 Loss is widely used, it may not always be the best choice, especially in situations where outliers are present. In such cases, alternative loss functions like L1 Loss (平均絶対誤差) やHuber損失の方が適している場合もあります。