Regresión Lasso
Lazo Regresión, which stands for Least Absolute Shrinkage and Selection Operator, is a type of regresión lineal that incorporates regularization to enhance prediction accuracy and interpretability in modelos estadísticos. It is particularly useful when dealing with datasets that have many features or variables.
El objetivo principal de la regresión Lasso es minimizar la función de pérdida 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.
Matemáticamente, la regresión Lasso resuelve lo siguiente de optimización:
minimize ||y - Xβ||² + λ||β||₁
Here, y represents the target variable, X is the matriz de características, 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 mejorar la precisión de la predicción. It is particularly effective in high-dimensional datasets where the number of predictors exceeds the number of observations.