Section 2
Random Forests
6. Ensemble Methods: Machine Learning and Democracy
4:57 (Preview)
7. Random Forests: Decisions Don't Fall Far from the Decision Tree 📂
15:38
8. Random Forests out in the Wild 📂
36:15
9. Interpolation Through a Random Forest 📂
08:57
Section 3
Gradient Boosting
10. Give Yourself a Gradient Boost
07:01 (Preview)
11. Auto-Correction in a Forest of Stumps 📂
22:06
12. Gradient Boosting by Hand: Code Example 📂
15:55
13. XGBoost in the Wild 📂
14:41
14. Cross validate with the XGBoost API 📂
15:30
15. Conclusion, Certificate, and What Next?
05:07
6. Ensemble Methods: Machine Learning and Democracy
Random Forests

Welcome to the forth section of the course. In this section we shall tackle the short-comings of the humble decision tree by introducing a new class of models: ensemble methods. The key idea is to take collections of "weak" machine learning models and build an aggregate model in a statistically rigorous way.

In this section, we shall explore the most popular prototype: a random forest. In this case, the weak learners are small decision tress which act together, democratically, to make robust and generalisable predictions.

Decision Tree Vs. Random Forest

Figure 1. Compare of the performance of a decision tree (left) with a random forest of decision trees (right).

We shall cover:

  • Introduction to the notion of ensemble models.
  • Key concepts and terminology.
  • Worked data examples: Random Forests.
  • Performative comparison with a single decision tree.
Next Lesson
7. Random Forests: Decisions Don't Fall Far from the Decision Tree