2.3.2 Decision Trees and Random Forests

Why This Sub-Part Matters

Decision Trees and Random Forests is the working skill inside Supervised Models that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result. A sub-part is now a folder so longer topics can grow without forcing everything into one huge page.

Study Pages

Page	Purpose
Deep Dive	Full explanation, mechanisms, examples, and failure modes.
Examples and Practice	Worked exercises, project drills, and self-check prompts.

Core Ideas

• Define Decision Trees and Random Forests in plain language before naming tools or frameworks.
• Connect it to the stage artifact: An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card.
• Measure it with: lift, calibration, feature importance, speed, and interpretability
• Name at least one failure mode, because real AI engineering is mostly controlled failure reduction.
• Keep the first implementation small enough to inspect by hand before scaling it.

How to Study It

1. Read this overview and write the concept in your own words.
2. Read the deep dive and identify the input, transformation, output, and failure mode.
3. Complete the examples and practice page.
4. Add one measurement using: Compare lift, calibration, feature importance, speed, and interpretability.

Completion Standard

• I can explain Decision Trees and Random Forests without naming a tool first.
• I can connect it to the stage artifact.
• I can show a small artifact, measurement, or test.
• I know how it fails and what I would inspect first.

Return to 2.3 Supervised Models.