Final answer:
LASSO regression provides variable selection and regularization, handles multicollinearity by zeroing out less important coefficients, and is computationally efficient, making it advantageous over traditional forward or backward selection methods.
Step-by-step explanation:
LASSO regression, which stands for Least Absolute Shrinkage and Selection Operator, has several advantages over traditional forward or backward selection methods.
One of the main benefits of LASSO regression is its ability to perform variable selection and regularization at the same time.
This helps in enhancing the prediction accuracy and interpretability of the statistical model by automatically selecting only the most significant variables and shrinking the coefficients of less important ones towards zero.
Another advantage is that LASSO can handle multicollinearity by penalizing the absolute size of coefficients, leading to some coefficients being exactly zero, which implies that the corresponding variables are not included in the model.
This is particularly useful when dealing with high-dimensional data where the number of predictors is greater than the number of observations, a scenario where traditional methods can fail.
Moreover, LASSO is computationally less intensive and tends to outperform forward and backward selection methods in terms of computational efficiency, especially in large datasets.