📅 2025-06-26 — Session: Analyzed Bias-Variance Tradeoff in Decision Trees
🕒 10:45–11:10
🏷️ Labels: Python, Machine Learning, Decision Trees, Bias-Variance
📂 Project: Teaching
⭐ Priority: MEDIUM
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.