📅 2025-07-15 — Session: Refactored Machine Learning Models for Improved Evaluation
🕒 19:00–19:30
🏷️ Labels: Machine Learning, Model Evaluation, Random Forest, Python, Classification
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to refine machine learning models by integrating robust evaluation metrics and handling multiclass and multi-target classification challenges.
Key Activities
- Refined the
fit_modelfunction to incorporate model evaluation metrics tailored for both classification and regression tasks. - Implemented 1-vs-Rest classification strategy to enhance performance in multiclass scenarios using Scikit-learn.
- Refactored code to support multi-target classification, focusing on RandomForestClassifier, and improved diagnostics for feature importance.
- Analyzed class imbalance issues within the model, providing insights and remediation strategies.
- Conducted a detailed performance analysis of a classifier predicting marital status, identifying challenges with specific categories.
Achievements
- Successfully integrated evaluation metrics into the
fit_modelfunction. - Enhanced model performance through 1-vs-Rest classification and improved multi-target handling.
- Identified and proposed solutions for class imbalance issues.
Pending Tasks
- Further testing and validation of the refactored models to ensure robustness across different datasets.
- Implement additional strategies to address class imbalance, such as resampling techniques.