Which statistical learning tool is considered less flexible compared to others in the context of regression analysis?

In the context of regression analysis, Lasso Regression is recognized for its ability to perform both variable selection and regularization to enhance the accuracy of predictions and improve model interpretability. However, when considering flexibility, Lasso Regression is viewed as less flexible than other methods, particularly those that do not impose such strict penalties on the number of variables included in the model.

Lasso works by adding a penalty equivalent to the absolute value of the magnitude of coefficients, which can shrink some coefficients entirely to zero. This characteristic makes Lasso particularly effective in sparse data contexts, where many predictors may not contribute meaningfully to the outcome. While this can be beneficial, it limits the flexibility of the model since it can exclude potentially useful predictors.

In contrast, tools like boosting, Ridge regression, and linear regression do not enforce this level of sparsity and can adapt to a wider range of relationships in the data. Boosting combines the strengths of multiple weak models to create a robust prediction outcome, thus exhibiting great flexibility to model complex data patterns. Ridge regression mitigates the issue of multicollinearity but keeps all coefficients, thus retaining all predictors in the model without elimination. Linear regression, being one of the simplest forms, can also accommodate a relatively straightforward relationship between the independent