Explored Regression Techniques for MSE-Trained Ensembles

• Day: 2025-10-05
• Time: 17:00 to 18:10
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Regression, Machine Learning, Quantile Regression, MSE, Bias-Variance

Description

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.

Evidence

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