Conducting a linear regression model using a loss function, why should I use $L_1$ instead of $L_2$ regularization?
Is it better at preventing overfitting? Is it deterministic (so always a unique solution)? Is it better at feature selection (because producing sparse models)? Does it disperse the weights among the features?