Enhanced Matplotlib Visualizations and Feature Importance
- Day: 2023-01-13
- Time: 14:30 to 16:00
- Project: Dev
- Workspace: WP 2: Operational
- Status: Completed
- Priority: MEDIUM
- Assignee: Matías Nehuen Iglesias
- Tags: Matplotlib, Data Visualization, Feature Importance, Python, Pandas
Description
Session Goal
The session aimed to enhance [[data visualization]] techniques using Matplotlib and explore methods for determining feature importance in machine learning models.
Key Activities
- Explored methods to modify x-tick labels in Matplotlib, including setting, customizing, and rotating labels to prevent overlap.
- Implemented techniques for setting multi-index in Pandas DataFrames and renaming index axes.
- Examined methods for determining feature importance in classifiers, focusing on Permutation Importance and RandomForestClassifier.
- Generated scatter plots and dual bar charts using Matplotlib and Pandas, emphasizing feature importance and correlation visualization.
Achievements
- Successfully modified x-tick labels in Matplotlib, enhancing readability and presentation of data visualizations.
- Applied Pandas techniques for multi-indexing and index manipulation, improving data handling capabilities.
- Clarified multiple methods for assessing feature importance, providing practical coding examples for implementation.
Pending Tasks
- Further exploration of advanced visualization techniques and feature importance methods in different machine learning models.
Evidence
- source_file=2023-01-13.sessions.jsonl, line_number=1, event_count=0, session_id=dc4ad282fdba586a8f5d5a662a1892024d91c3c75592fc83dde6f353877c40df
- event_ids: []