Comprehensive Evaluation of Classifier Models

2 min read

📅 2025-07-15 — Session: Comprehensive Evaluation of Classifier Models

🕒 19:30–20:00
🏷️ Labels: Classifier Evaluation, Model Tuning, Class Imbalance, Income Prediction, Machine Learning
📂 Project: Dev
⭐ Priority: MEDIUM

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.

Graph View

Backlinks