Analyzed Bias-Variance Tradeoff in Decision Trees

• Day: 2025-06-26
• Time: 10:45 to 11:10
• Project: Teaching
• Workspace: WP 1: Strategic / Growth & Development
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Machine Learning, Decision Trees, Bias-Variance

Description

Session Goal: The session aimed to explore the bias-variance tradeoff in decision tree regression models using Python.

Key Activities:

• Developed a Python notebook cell for visualizing decision tree regression models with varying maximum depths (2, 3, and 5) using noisy sine data.
• Implemented a systematic framework to empirically analyze the bias-variance tradeoff with synthetic datasets.
• Provided a detailed explanation of the bias-variance decomposition, including mathematical formulation and a Python function to compute bias and variance from predictions.

Achievements: Successfully visualized decision tree models and established a framework for bias-variance analysis, enhancing understanding of model performance.

Pending Tasks: Further exploration of different model parameters and datasets to generalize findings.

Evidence

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• event_ids: []