Practice — Regularization: L1, L2 and Elastic Net (5 questions)

You are modelling customer churn with a linear model. Among your 40 features there is a block of six that are strongly correlated with each other (pairwise correlation 0.85–0.95): sessions_7d, sessions_14d, sessions_30d, sessions_60d, sessions_90d, sessions_180d. All six are individually predictive of churn.

You fit an unpenalised model, a Ridge model and a Lasso model, tuning \lambda by 5-fold CV in each case.

1. Describe what you expect the six coefficients to look like under each of the three models, and explain the mechanism behind each pattern.
2. A colleague reruns the Lasso on a different random 5-fold split and gets a different member of the block selected. Is the model broken? What would you recommend instead, and why?
3. If your goal is interpretation ("which activity window matters most?"), which of the three models is the most misleading, and why?