Sparse regression is a statistical technique used to identify a small subset of the most relevant predictors or variables in a dataset that have the most impact on the outcome variable. This method aims to simplify and improve the interpretability of regression models by reducing the number of variables included in the model while still maintaining a good level of predictive accuracy. Sparse regression techniques often involve penalizing the coefficients of the irrelevant variables or using feature selection methods to identify the most important variables. This approach is commonly used in various fields such as machine learning, finance, genetics, and neuroscience.