# Why lasso for feature selection?

Suppose I have a high-dimensional dataset and want to perform feature selection. One way is to train a model capable of identifying the most important features in this dataset and use this to throw away the least important ones.

In practice I would use sklearn's SelectFromModel transformer for this. According to the documentation any estimator with either a feature_importances_ or a coef_ attribute would do.

Besides Lasso, many other linear models have this attribute (LinearRegression, Ridge and ElasticNet to name a few) and can be used for identifying the most important features.

What makes Lasso the most popular model for identifying the most important features in a dataset?