📅 2024-10-06 — Session: Enhanced Machine Learning Model Evaluation and Improvement
🕒 00:00–00:20
🏷️ Labels: Machine Learning, Classification, Model Evaluation, Python, Feature Engineering
📂 Project: Dev
⭐ Priority: MEDIUM
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.