Least Absolute Shrinkage and Selection Operator (LASSO) regression, is a regularization technique used in regression cases where the model overfits or there is high multi-collinearity.
Lease Absolute Shrinkage and Selection Operator (LASSO) regression, is a regularization technique used in regression cases where the model overfits or there is high multi-collinearity. It has one tuning parameter, $\lambda$, and as this value in increased the estimates are shrunk closer and closer to zero. It differs from Ridge Regression in that values can be shrunk to zero which can make this Lasso Regression useful for feature selection.
It is defined by:
$$SSE_{L1 norm} = \sum_{i=1}^{n}(y_i-\hat{y_i})^2 + \lambda \sum_{j=1}^{P} \lvert{\beta_j^2}\rvert$$
Where the goal is to reduce model complexity and by adding a penalty term to the Sum of Squared Errors (SSE).
