📅 2025-10-05 — Session: Explored Regression Techniques for MSE-Trained Ensembles
🕒 17:00–18:10
🏷️ Labels: Regression, Machine Learning, Quantile Regression, MSE, Bias-Variance
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore challenges and solutions in using MSE-trained ensembles, particularly random forests, for regression tasks.
Key Activities
- Discussed the distortion of distributions and bias-variance tradeoffs in MSE-trained ensembles.
- Explored actionable solutions like quantile regression forests and distributional modeling to enhance predictive accuracy.
- Outlined various modeling techniques for income distribution, focusing on handling zero values and heavy tails.
- Discussed methods for calculating level means from quantile models using distribution-agnostic techniques.
Achievements
- Developed insights into regression techniques that address the limitations of MSE-trained ensembles.
- Compiled a ranked list of income modeling approaches with practical implementation steps.
- Clarified methods for calculating level means from quantile models.
Pending Tasks
- Further exploration of quantile regression techniques in practical scenarios.
- Implementation of discussed methods in a real-world dataset to validate insights.