Enhanced Machine Learning Model Evaluation and Improvement

• Day: 2024-10-06
• Time: 00:00 to 00:20
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Machine Learning, Classification, Model Evaluation, Python, Feature Engineering

Description

Session Goal

The session aimed to resolve issues related to machine learning model evaluation and to propose enhancements for an email classification model.

Key Activities

• Fixing CountVectorizer Input Error: Addressed an error in Python where multiple DataFrame columns needed to be combined for text vectorization using CountVectorizer.
• Classifier Performance Analysis: Conducted a detailed analysis of a classifier’s performance, identifying strengths and weaknesses, and provided recommendations for accuracy improvement.
• Identifying Misclassified Cases: Implemented a Python script to list cases with prediction errors by comparing true and predicted labels.
• Handling Sparse Matrices: Developed a method to maintain index integrity during train-test splits to better handle misclassified samples in sparse matrices.
• Improving Email Classification Model: Explored strategies for enhancing an email classification model, including TF-IDF vectorization, n-grams, feature engineering, and a multi-layer model approach.

Achievements

• Successfully fixed the CountVectorizer input error.
• Gained insights into classifier performance and identified areas for improvement.
• Developed robust methods for identifying misclassified cases and handling sparse matrices.

Pending Tasks

• Implement the proposed strategies for improving the email classification model, focusing on feature engineering and advanced model architectures.

Evidence

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• event_ids: []