Refactored and Evaluated Machine Learning Classifiers

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📅 2025-07-15 — Session: Refactored and Evaluated Machine Learning Classifiers

🕒 19:00–20:00
🏷️ Labels: Machine Learning, Model Evaluation, Classification, Gradient Boosting, Python
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary objective of this session was to refine, implement, and evaluate various machine learning classifiers to improve their performance, particularly focusing on handling class imbalance and optimizing model selection.

Key Activities

  • Refined the fit_model function to include an evaluation integration, allowing for modular evaluation across different model types (classification and regression).
  • Implemented 1-vs-Rest classification for multiclass variables using Scikit-learn, enhancing the model’s ability to handle multiclass scenarios.
  • Refactored code to support multi-target classification with per-column diagnostics, improving the independent evaluation of each target variable.
  • Conducted a comprehensive analysis of class imbalance issues in model predictions, with recommendations for using techniques like SMOTE.
  • Evaluated the performance of various classifiers, including those for marital status, income prediction, and labor market analysis, identifying strengths, weaknesses, and areas for improvement.
  • Explored the impact of class_weight='balanced' on classification results, assessing its effect on accuracy, recall, and precision.
  • Proposed replacing RandomForestClassifier with Gradient Boosting Models like XGBoost and LightGBM for better performance in structured data scenarios.

Achievements

  • Successfully integrated evaluation functions into the model fitting process.
  • Improved understanding of class imbalance impacts on model performance and identified strategies to mitigate these issues.
  • Identified key areas for model improvement and optimization, particularly through the use of advanced ensemble methods.

Pending Tasks

  • Further tuning of gradient boosting models to ensure optimal performance.
  • Continuous monitoring and adjustment of class weights to maintain balanced model predictions.

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