Rank the following statistical learning tools based on their flexibility from most to least flexible.

Boosting, Linear Regression, and Lasso Regression can be ranked based on their flexibility as follows:

Boosting is the most flexible method among the three. It is an ensemble technique that combines multiple weak learners, typically decision trees, to form a strong predictive model. This method can capture complex relationships in the data and adapt to patterns that may not be easily identified by simpler models. By sequentially weighing instances and focusing on the errors made by previous models, boosting can significantly improve performance in various scenarios.

Linear Regression follows in terms of flexibility. It is a parametric model that assumes a linear relationship between the dependent and independent variables. While it can effectively model relationships in the data, its flexibility is limited because it only captures linear patterns. If the true relationship in the data is non-linear, linear regression may not perform as well compared to more flexible methods like boosting.

Lasso Regression, while it also allows for flexibility through regularization and can handle high-dimensional datasets, is generally considered to be less flexible than both boosting and linear regression in terms of capturing complex interactions. Lasso's primary function is to penalize the absolute size of the coefficients, leading to variable selection and potentially simpler models. While it can control overfitting, it is still constrained