Classical ML, validation, metrics, interpretation, debugging · insight-1163
Plot the coefficient path before naming the drivers a lasso model selected
- Evidence type
- documented fact
- Difficulty
- medium
- Roles
- Data scientist, Data analyst
- Industries
- cross-industry
Summary: situation, action, reason, boundary
Situation
A lasso fit at one alpha keeps eight features and the report calls them the key drivers.
Action
Compute the full path (lasso_path or a CV path), plot coefficients against log alpha, and report the order of entry plus how many features stay selected over the near-optimal range.
Reason
The path shows which features enter first and which are fragile, which a single fit cannot.
Boundary
Sparse linear and logistic models used for explanation or feature screening.
Symptom or risk
The order in which features enter, and whether a coefficient changes sign or sits near zero for most of the path, is invisible at a single alpha; the story depends on one arbitrary penalty.
Exceptions
For prediction-only use, one tuned alpha is enough.
Trade-offs
Reading paths takes judgement; summarize as entry order and stability counts.
Pitfalls
Interpreting shrunken coefficients as unbiased effect sizes; refit an unpenalized model on the selected set for effect estimates, with selection uncertainty noted.
Lessons and concepts
Claims and their sources
scikit-learn's lasso_path computes the regularization path with coordinate descent and returns the coefficients (n_features by n_alphas) for each alpha on an automatic grid.