Finalized Ridge/Lasso thesis notebook and diagnostics

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Finalized Ridge/Lasso thesis notebook and diagnostics

  • Day: 2026-06-10
  • Time: 11:45 to 12:35
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Ridge, Lasso, Regularization, Thesis, Notebook, Diagnostics

Description

Session Goal

Refine the thesis regularization chapter by turning Ridge and Lasso into a diagnostic scaffold rather than a pure model-selection contest, while also finalizing a notebook workflow that can be executed from existing artifacts.

Key Activities

  • Reframed Ridge/Lasso results as evidence about shrinkage, sparsity, coefficient compression, and bias-variance tradeoffs, not as a prediction breakthrough over OLS.
  • Audited the preprocessing pipeline to assess whether the current design is suitable for interpreting regularization paths.
  • Identified a methodological gap: numeric features are standardized, but one-hot categorical dummies are not, which affects coefficient comparability.
  • Proposed a standardized-design variant and clarified collinearity / drop-first implications for interpretation.
  • Designed a thesis-ready notebook structure using existing run artifacts: setup, CV summaries, coefficient-path interpretation, tails, and approximate bias-variance proxies.
  • Defined a reproducible artifact audit plan to inspect configs, schemas, metrics, CV outputs, coefficient artifacts, and missing diagnostics.
  • Added book-style visualization logic using relative coefficient norms and normalized CV error proxies to make the regularization story more interpretable.
  • Finalized the notebook iteration with exported figures and tables, and preserved intermediate tables for continued review.

Achievements

  • Established a coherent thesis narrative: regularization is useful as a diagnostic lens, but the best Ridge/Lasso settings remain close to OLS in predictive performance.
  • Clarified that mild sparsity can be obtained with limited performance loss, supporting a parsimonious interpretation.
  • Flagged P09 categories as influential enough to justify a category-level audit and sensitivity analysis.
  • Documented the notebook/output structure and the expected artifact locations for reproducible analysis.
  • Identified backend gaps that may require future implementation, especially coefficient-path exports by alpha and compression diagnostics.

Pending Tasks

  • Run the proposed P09 sensitivity / category-level audit.
  • Regenerate any missing compression-distribution CSV if needed.
  • Decide whether to implement backend support for coefficient-path exports and additional diagnostics.
  • Apply notebook figure fixes and thesis revisions based on the new diagnostic framing.

Evidence

  • source_file=2026-06-10.sessions.jsonl, line_number=2, event_count=0, session_id=6b275e34b0bcb8482a2e1463f101e6cac2b6970a26588da3a026dc75565962d3
  • event_ids: []

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