Data Science, Regularization, Machine Learning, Editors Pick, Hands On Tutorials.
has a lot of features and all have a relatively similar weight in the model, i.e., their coefficient values are very similar. Conclusion Depending on the problem you’re working on, it might be more useful to have a model that’s more interpretable compared to a lower variance, yet overly complex,