📅 2025-10-20 — Session: Refined EPH Regression and Model Evaluation
🕒 00:30–02:40
🏷️ Labels: Feature Engineering, Model Evaluation, Eph Regression, Multi-System, Documentation
📂 Project: Dev
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.