📅 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.