Random Forest and XGBoost

Lesson Overview

In "Machine Learning for Trading" → "Model Training", this lesson "Random Forest and XGBoost" builds knowledge you can apply immediately.

Tree models capture non-linearity. Train RF/XGB on train, evaluate on test. Analyze feature importance.

Core Concepts

These points align with the video walkthrough—take notes as you watch.

• ▸Tree models capture non-linearity.
• ▸Train RF/XGB on train, evaluate on test.
• ▸Analyze feature importance.

Practical Application

After studying, complete the companion practice lesson to move from understanding to doing.

• ▸Follow along in Jupyter or VS Code
• ▸Log how parameter changes affect backtest results
• ▸Submit a reproducible analysis notebook

FAQ

• ▸Q: How does this lesson fit the course? A: Part of "Model Training"—follow the curriculum order.
• ▸Q: What do I need? A: Demo account and notes; quant lessons need a Python environment.
• ▸Q: How do I know I've mastered it? A: Pass the quiz and explain three key points of Random Forest and XGBoost on your own.