📅 2025-06-26 — Session: Decision Tree Regression and Bias-Variance Analysis
🕒 10:45–11:10
🏷️ Labels: Python, Machine Learning, Decision Trees, Bias-Variance, Data Visualization
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal
The primary aim of this session was to explore decision tree regression models with varying depths and to analyze the bias-variance tradeoff using synthetic datasets.
Key Activities
- Developed a Python notebook cell to visualize decision tree regression models with varying maximum depths (2, 3, and 5) on noisy sine data.
- Outlined a framework for analyzing the bias-variance tradeoff, including code examples for implementation and visualization.
- Provided a detailed explanation of the bias-variance decomposition, including mathematical formulation and Python function to compute bias and variance.
Achievements
- Successfully visualized decision tree regression models with different depths.
- Established a systematic approach to analyze bias-variance tradeoff.
- Explained bias-variance decomposition with practical Python implementation.
Pending Tasks
- Further exploration of other regression models for bias-variance analysis.
- Validation of results with additional datasets.