Prepared thesis diagnostics for Ridge, Lasso, and HGB

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Prepared thesis diagnostics for Ridge, Lasso, and HGB

  • Day: 2026-06-10
  • Time: 11:45 to 12:30
  • Project: Dev
  • Workspace: WP 1: Strategic / Growth & Development
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Ridge, Lasso, Hgb, Thesis, Diagnostics, Notebooks

Description

Session Goal

Refine the thesis modeling strategy for income prediction by treating Ridge/Lasso and HistGradientBoosting (HGB) as diagnostic tools rather than competing final winners. The work aimed to clarify how each model family should be interpreted in the thesis, how to structure the narrative across chapters, and how to operationalize notebook-based evaluation without re-training unnecessary artifacts.

Key Activities

  • Reframed Ridge and Lasso as evidence for the limits of linear models: weak regularization behaves similarly to OLS, stronger shrinkage mainly increases underfitting, and sparsity gains are limited.
  • Identified the need to inspect stable coefficient patterns, distributional compression, and dominant P09 category effects before drawing final conclusions.
  • Drafted thesis-oriented guidance on how to distribute theory, empirical results, and interpretation across methodology, results, and discussion chapters.
  • Produced thesis-ready LaTeX support material, including concise Spanish captions for figures and a table about Ridge/Lasso trajectories, prediction compression, and decile-level error patterns.
  • Defined the scientific role of HGB as a nonlinear diagnostic bridge: testing nonlinearity, interaction structure, capacity control, and distributional error patterns.
  • Established an execution workflow for HGB notebook work: discover existing runs first, load artifacts, avoid re-training in notebooks, and generate a minimal robust notebook before expanding diagnostics and visualization.
  • Corrected the notebook logic to distinguish run roles such as benchmark, cv_sweep, and directed_sweep, so missing artifacts are not treated as failures when they are not applicable.

Achievements

  • Clarified the thesis narrative: linear regularization methods are best presented as diagnostic regularization within the linear-model family.
  • Mapped HGB experiments to specific research questions and thesis outputs, giving the nonlinear section a clearer methodological purpose.
  • Established a robust notebook strategy for auditing runs and artifacts, reducing ambiguity around missing outputs and improving reproducibility.
  • Generated reusable LaTeX captions and structural guidance that can be inserted directly into the thesis draft.

Pending Tasks

  • Audit the dominant P09 category effects before final interpretation of the linear-model results.
  • Consolidate the Ridge/Lasso evidence into the thesis chapters with the proposed diagnostic framing.
  • Continue building the HGB notebook from existing runs, prioritizing stable evidence tables and benchmark summaries before adding richer plots.
  • Verify that all run types have the correct artifact expectations and that the notebook remains robust to missing optional outputs.

Evidence

  • source_file=2026-06-10.sessions.jsonl, line_number=1, event_count=0, session_id=61f46d0b852df603c0499bbce0580bd9b2c0ce9a1684ec6095efe68241d7280a
  • event_ids: []

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