LASSO Regression
A regularisation method is lasso regression. For a more accurate forecast, it is preferred over regression techniques. Shrinkage is used in this model. When data values shrink towards the mean, this is referred to as shrinkage. Models with fewer parameters are encouraged by the lasso technique since they are straightforward and sparse. When a model exhibits a high degree of multicollinearity or when you wish to automate some steps in the model selection process, such as variable selection and parameter removal, this specific sort of regression is ideally suited.
L1 regularisation technique is used in Lasso Regression (explained more in this article). Because it does feature selection automatically, it is employed when there are more features.
Here’s a step-by-step explanation of how LASSO regression works:
- Linear Regression Model: A linear relationship between the independent variables (features) and the dependent variable (target) is assumed in the linear regression model, which is where LASSO regression begins.
- L1 regularisation: LASSO regression adds a second penalty term depending on the absolute coefficient values. The absolute values of the coefficients are added together and then multiplied by a tuning parameter to create the L1 regularisation term.
- Objective function: Finding the values of the coefficients that minimise the sum of the squared differences between the predicted values and the actual values while also minimising the L1 regularisation term is the goal of LASSO regression.
- Shrinking Coefficients: Coefficients can be shrunk towards zero in LASSO regression by including the L1 regularisation component. Some coefficients are driven to exactly zero when is large enough. The variables with zero coefficients are efficiently eliminated from the model thanks to this characteristic of LASSO, which makes it beneficial for feature selection.
- Tuning Parameter: In LASSO regression, the regularisation parameter’s selection is critical. More coefficients will be pushed towards zero as regularisation increases with increasing values. A smaller value, on the other hand, lessens the regularisation impact, allowing more variables to have coefficients that are not zero.
- Model fitting: An optimisation approach is employed to minimise the objective function in order to estimate the coefficients in the LASSO regression. It is usual practise to use Coordinate Descent, which fixes the other coefficients while iteratively updating each coefficient.
How to do nested cross-validation with LASSO in caret or tidymodels?
Nested cross-validation is a robust technique used for hyperparameter tuning and model selection. When working with complex models like LASSO (Least Absolute Shrinkage and Selection Operator), it becomes essential to understand how to implement nested cross-validation efficiently. In this article, we’ll explore the concept of nested cross-validation and how to implement it with LASSO using popular R packages, Caret and Tidymodels.