Regressão Lasso
Laço Regressão, which stands for Least Absolute Shrinkage and Selection Operator, is a type of regressão linear that incorporates regularization to enhance prediction accuracy and interpretability in modelos estatísticos. It is particularly useful when dealing with datasets that have many features or variables.
O objetivo principal da Regressão Lasso é minimizar a função de perda 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.
Matematicamente, a Regressão Lasso resolve o seguinte problema de otimização:
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 melhorar a precisão da previsão. It is particularly effective in high-dimensional datasets where the number of predictors exceeds the number of observations.