Optimized Multi-Output Classification with XGBoost

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Optimized Multi-Output Classification with XGBoost

  • Day: 2025-07-15
  • Time: 20:10 to 20:45
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Xgboost, Multi-Output, Classification, Error Diagnosis, Machine Learning

Description

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.

Evidence

  • source_file=2025-07-15.sessions.jsonl, line_number=3, event_count=0, session_id=ac50e16582bd76aa82a209fb9e118192fcc3d8d4d8727a2881a3f03a27d951ed
  • event_ids: []

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