📅 2025-07-15 — Session: Refactored and Evaluated Machine Learning Models
🕒 20:05–20:45
🏷️ Labels: Machine Learning, Model Evaluation, Python, Code Refactoring, Xgboost, Histgradientboostingclassifier
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to address and resolve issues related to multi-output classification using XGBoost, refactor Python code for model training, and evaluate various classification models.
Key Activities
- XGBoost Multi-Output Classification Error Handling: Explored solutions for handling errors in XGBoost multi-output classification by training separate models or using a Scikit-learn wrapper.
- ValueError in HistGradientBoostingClassifier: Addressed a ValueError in HistGradientBoostingClassifier with multi-label targets, suggesting separate classifiers or MultiOutputClassifier.
- Python Code Refactoring: Refactored code for model training to improve modularity and reduce redundancy.
- Model Evaluation Updates: Updated the
evaluate_modelfunction for single-target evaluation, simplifying code. - Model Performance Analysis: Analyzed CAT_OCUP model performance, highlighting class imbalance issues and recommending improvements.
- Comparative Analysis of Models: Conducted detailed comparisons between various models (e.g., CAT_INAC, CH07) using metrics like F1 score and accuracy.
Achievements
- Successfully refactored model training code for better performance.
- Evaluated and compared multiple models, identifying areas for improvement.
Pending Tasks
- Further optimization of models based on the analysis, particularly focusing on class imbalance and underperforming classes.