Comprehensive Evaluation of Classifier Models

• Day: 2025-07-15
• Time: 19:30 to 20:00
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Classifier Evaluation, Model Tuning, Class Imbalance, Income Prediction, Machine Learning

Description

Session Goal: The session aimed to evaluate and refine multiple binary classification models used for predicting various income and benefit-related variables, with a focus on addressing class imbalance and improving model accuracy.

Key Activities:

• Evaluated the performance of the INGRESO classifier, analyzing confusion metrics and exploring potential causes of misclassification.
• Analyzed the INGRESO_NLB classifier to identify strengths and weaknesses, and provided recommendations for improving accuracy and recall.
• Reviewed the classifier for pension income, suggesting refinements for enhanced accuracy.
• Investigated the collapse of the INGRESO_SBS model due to class imbalance, offering strategic recommendations for improvement.
• Examined the PP07G1 classifier, focusing on class imbalance and recall issues, with suggestions for accuracy improvements.
• Discussed modeling challenges for PP07G_59 and provided strategic recommendations for better detection of precarized workers.
• Evaluated the PP07I classifier, highlighting class imbalance issues and suggesting improvements.
• Analyzed the performance of classifiers for PP07K and PP07J, identifying class dominance issues and recommending strategies for improvement.
• Assessed the impact of class_weight='balanced' on classification results, noting improvements in minority class detection.
• Examined the catastrophic degradation of the clf3 model, suggesting immediate corrective strategies.

Achievements:

• Comprehensive evaluation of multiple classifiers, identifying key performance issues and providing actionable recommendations for improvement.
• Enhanced understanding of the impact of class imbalance and weighting strategies on model performance.

Pending Tasks:

• Implement recommended model refinements and re-evaluate performance.
• Explore alternative modeling approaches to address identified issues.
• Further investigate the impact of class weighting on different classifiers.

Evidence

• source_file=2025-07-15.sessions.jsonl, line_number=4, event_count=0, session_id=df57096fb89c5e1b643476e43d84da9545e1a2d6feeb85bf6c600c4fadf2288a
• event_ids: []