Optimized Multi-Output Classification with XGBoost

📅 2025-07-15 — Session: Optimized Multi-Output Classification with XGBoost

🕒 20:10–20:45
🏷️ Labels: Xgboost, Multi-Output, Classification, Error Diagnosis, Machine Learning
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to diagnose and resolve errors in multi-output classification using XGBoost and other machine learning models, while also optimizing model performance through refactoring and evaluation.

Key Activities

• Diagnosed and provided solutions for errors in XGBoost multi-output classification.
• Addressed ValueError in HistGradientBoostingClassifier with solutions for multilabel target matrices.
• Refactored Python code for Gradient Boosting Classifier to improve modularity.
• Fixed the evaluate_model function for single-target evaluation.
• Evaluated RandomForest outcomes, focusing on class imbalance issues.
• Analyzed classification model performance using F1 scores and accuracy metrics.
• Compared CAT_INAC models, highlighting performance changes with HistGradientBoostingClassifier.
• Compared CH07 models with a new booster, noting improvements in performance metrics.
• Conducted a structured comparison of HistGradientBoostingClassifier and RandomForestClassifier models.
• Developed strategies for multi-class classification in imbalanced datasets, emphasizing model ensembles and feature engineering.

Achievements

• Successfully refactored code to enhance modularity and performance.
• Improved understanding of model performance metrics and class imbalance handling.
• Developed comprehensive strategies for optimizing multi-class classification.

Pending Tasks

• Further exploration of hyperparameter optimization for improved model performance.
• Implementation of recommended strategies for class imbalance in future models.