📅 2024-10-06 — Session: Enhanced Machine Learning Model Evaluation and Improvement
🕒 00:00–00:20
🏷️ Labels: Machine Learning, Python, Model Evaluation, Dataframe, Classifier
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to address issues related to machine learning model evaluation and improvement, specifically focusing on handling input errors, classifier performance analysis, and improving email classification models.
Key Activities
- Fixing CountVectorizer Input Error: Implemented a solution to combine multiple DataFrame columns into a single text input for vectorization using CountVectorizer.
- Classifier Performance Analysis: Analyzed classifier performance, identifying strengths and weaknesses, and provided recommendations for accuracy and precision improvements.
- Identifying Misclassified Cases: Developed a Python script to identify misclassified cases by comparing predicted labels with true labels.
- Handling Sparse Matrices: Corrected handling of sparse matrices during the train-test split to maintain indexes for effective identification of misclassified samples.
- Improving Email Classification Model: Proposed strategies for improving email classification, including TF-IDF vectorization, n-grams, feature engineering, and utilizing a multi-layer model approach.
Achievements
- Successfully resolved the CountVectorizer input error.
- Provided actionable insights for classifier performance improvement.
- Identified misclassified cases effectively using a Python script.
- Enhanced handling of sparse matrices in machine learning workflows.
Pending Tasks
- Implement the proposed strategies for improving the email classification model.