Description: RidgeCV is a regression model used in the field of machine learning to address overfitting issues in linear models. Its main feature is the ability to automatically adjust the regularization parameter through a cross-validation process. This means that instead of requiring the user to manually specify the regularization value, RidgeCV evaluates different values and selects the one that best fits the data. This approach not only improves model accuracy but also simplifies the modeling process, making it more accessible to those who are not experts in the field. RidgeCV is based on the Ridge regression technique, which penalizes the magnitude of regression coefficients, helping to reduce model complexity and improve generalization. This method is particularly useful in situations where there is multicollinearity among predictor variables, as it allows for more stable and reliable estimates. In summary, RidgeCV combines the robustness of Ridge regression with the convenience of cross-validation, making it a valuable tool for data analysts and data scientists looking to build effective predictive models.
