L2正則化

L2正則化

L2 正則化, also known as Ridge Regularization, is a method 機械学習で使用される and statistics to prevent overfitting of models. Overfitting occurs when a model learns the noise in the 訓練データ 基本的なパターンではなく、見えないデータに対する性能が低下します。

L2正則化では、ペナルティ項が追加されます 損失関数, which is the function used to measure how well the model’s predictions match the actual outcomes. Specifically, the penalty is proportional to the square of the magnitude of the coefficients (weights) of the model. This means that larger weights contribute more to the penalty, effectively discouraging the model from fitting the training data too closely.

L2正則化項は、次のように数学的に表されます：

Penalty = λ * Σ(w_i²)

ただし：

• λ (lambda) is the regularization parameter that controls the strength of the penalty.
• w_i represents each weight モデル内の

By adjusting the value of λ, practitioners can find a balance between fitting the training data well and keeping the model simple, which enhances its ability to generalize to 新しいデータ.

全体として、L2正則化は モデルの性能向上に不可欠です 過剰適合のリスクを減らすことで、より堅牢な予測に役立ちます。