Refactored Machine Learning Models for Improved Evaluation

• Day: 2025-07-15
• Time: 19:00 to 19:30
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Machine Learning, Model Evaluation, Random Forest, Python, Classification

Description

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_model function 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_model function.
• 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.

Evidence

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• event_ids: []