Nader - Tue, 29 Apr 2025 15:29:58 GMT - Untitled
Date: 2025-04-29 · Duration: 19.90999984741211 min Organizer: Nader Attendees: N/A
Summary
- Goal: Identify top factors influencing target column (‘decline or no decline’ boolean) in data frame; critical for predictive analysis.
- Data Inputs: Framework requires data frame, target column, and chosen analysis method; emphasize tree-based models due to their NA handling capabilities.
- Data Prep: Focus on tree-based approaches, perform one-hot encoding for categorical vars, and avoid premature NA treatment; essential for preparing clean input for model training.
- Feature Engineering: Must use only historical data to prevent leakage; important to create lag features and count features to enhance model accuracy.
- Modeling Strategy: Opt for tree-based classification models; K-fold cross-validation (10-fold) recommended for robust evaluation, ensuring consistency in results.
- Feature Analysis: Multiple methods to evaluate feature importance; SHAP values highlighted as the best approach for visualizing impact of features on predictions.
- Code Guidelines: Ensure code is configurable; prefer functions for feature engineering to maintain flexibility over hardcoded methods.
Action Items:
- Ahmadreza Abdoli: Copy key transcript sections and outline flow (due: 13:19); implement classification model and visualizations with SHAP values (due: 14:52, 16:35).
Action Items
- Ahmadreza Abdoli Copy the useful parts of the transcript and determine the flow to follow (13:19) Put the determined flow into Gemini to see what it recommends (13:19) Implement the tree-based classification model with cross-validation (14:52) Create visualizations for feature contributions using multiple methods including SHAP values (16:35)