ラッソ回帰

ラッソ回帰

ラッソ 回帰, which stands for Least Absolute Shrinkage and Selection Operator, is a type of 線形回帰 that incorporates regularization to enhance prediction accuracy and interpretability in 統計モデル. It is particularly useful when dealing with datasets that have many features or variables.

ラッソ回帰の主な目的は、最小化することです 損失関数 of the linear model while also imposing a penalty on the absolute size of the coefficients. This penalty term, known as L1 regularization, encourages the model to shrink some coefficients to zero, effectively performing variable selection. This means that Lasso Regression can help identify the most significant predictors in a dataset and discard irrelevant or less impactful features.

数学的に、ラッソ回帰は次の 最適化問題です:

minimize ||y - Xβ||² + λ||β||₁

Here, y represents the target variable, X is the 特徴行列, β denotes the coefficient vector, ||y - Xβ||² is the residual sum of squares, and λ is the regularization parameter that controls the strength of the penalty. A larger value of λ results in more coefficients being shrunk towards zero, which can help reduce overfitting but may also lead to underfitting if set too high.

Lasso Regression is widely used in fields such as finance, biology, and machine learning for its ability to simplify models and 予測精度を向上させる. It is particularly effective in high-dimensional datasets where the number of predictors exceeds the number of observations.