Random Forest 与 XGBoost

Lesson Overview

In "Machine Learning for Trading" → "模型训练", this lesson "Random Forest 与 XGBoost" builds knowledge you can apply immediately.

随机森林对多棵随机化树作集成，XGBoost按残差/梯度顺序加树，训练机制不同。配套可运行实现为随机森林，不把它冒充XGBoost训练证据。 运行ml_lab.py→核时间切分→与多数类基线比较→阅读树深/叶子样本限制；XGBoost另列未运行而非推断同结果。 本课作业：给一个两模型比较实验设计，至少固定数据、分界、指标和调参次数，说明本课实际运行哪种。

Core Concepts

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

• ▸随机森林对多棵随机化树作集成，XGBoost按残差/梯度顺序加树，训练机制不同。配套可运行实现为随机森林，不把它冒充XGBoost训练证据。
• ▸运行ml_lab.py→核时间切分→与多数类基线比较→阅读树深/叶子样本限制；XGBoost另列未运行而非推断同结果。
• ▸本课作业：给一个两模型比较实验设计，至少固定数据、分界、指标和调参次数，说明本课实际运行哪种。

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 "模型训练"—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 与 XGBoost on your own.