Refined EPH Regression and Model Evaluation

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Refined EPH Regression and Model Evaluation

  • Day: 2025-10-20
  • Time: 00:30 to 02:40
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: In Progress
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Feature Engineering, Model Evaluation, Eph Regression, Multi-System, Documentation

Description

Session Goal

The session aimed to refine the EPH regression workflow and evaluate model performance for income prediction, focusing on feature engineering, model training, and evaluation.

Key Activities

  • Conducted a script analysis for feature engineering and model training, identifying issues such as label leakage and categorical handling.
  • Configured test matrices for model predictions using Python, handling DataFrames and NumPy arrays.
  • Adjusted visualization techniques for RegressorChain models to improve metrics clarity.
  • Analyzed model performance for income prediction, identifying biases and outlining future refinement plans.
  • Summarized the EPH regression workflow, detailing the predictive pipeline and recent developments.
  • Reviewed the HistGradientBoostingRegressor, highlighting its advantages for specific modeling scenarios.
  • Structured project briefings and runbooks for multi-system projects, focusing on operational clarity.
  • Enhanced definitions for ‘Briefing’ and ‘Runbook’ in multi-system contexts, emphasizing integration and consistency.
  • Mapped data components for electoral analysis, proposing key files for briefing and runbook creation.
  • Outlined Bash commands for displaying file modification times, aiding in project documentation.

Achievements

  • Improved clarity and functionality of feature engineering and model training scripts.
  • Enhanced visualization and evaluation techniques for model predictions.
  • Developed a comprehensive understanding of the EPH regression workflow and HGBR advantages.
  • Structured documentation for multi-system projects, enhancing operational clarity.

Pending Tasks

  • Implement outlined refinements in income modeling to address identified biases.
  • Finalize the creation of coherent briefings and runbooks for electoral data analysis.
  • Continue refining the integration points and consistency across multi-system definitions.

Evidence

  • source_file=2025-10-20.sessions.jsonl, line_number=0, event_count=0, session_id=ec971bebaacaf2af05918d125a853217c2bf271d8941f6ffad3739c73acc7678
  • event_ids: []

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