You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative…

L1 regularization, also known as Lasso regularization, adds the sum of the absolute values of the

model’s coefficients to the loss function1. It encourages sparsity in the model by shrinking some

coefficients to precisely zero2. This way, L1 regularization can perform feature selection and remove

the non-informative features from the model while keeping the informative ones in their original

form. Therefore, using L1 regularization is the best technique for this use case.

Reference:

Regularization in Machine Learning - GeeksforGeeks

Regularization in Machine Learning (with Code Examples) - Dataquest

L1 And L2 Regularization Explained & Practical How To Examples

L1 and L2 as Regularization for a Linear Model