Navigation: <-- Part V: Supervised Learning | Main Index | Part VII: Unsupervised Learning -->

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Part VI: Principles That Transfer (Reflection)

After your first end-to-end pass through supervised learning, this part steps back to name the failure modes and principles that apply to every project, regardless of method. Overfitting, premature complexity, missing baselines, misaligned metrics, and unexplained predictions are not quirks of one algorithm — they are the recurring challenges of the field. The concepts here transfer to every subsequent part and every real project you will work on.

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Nuggets in This Part

#	Nugget	Prerequisites
1	Generalization	Classification Tasks · Data Splits · Underfitting and Overfitting
2	Start Simple	Generalization
3	Baselines and the Good-Enough Bar	Start Simple
4	Choosing and Aligning Metrics	Baselines and the Good-Enough Bar · Classification Evaluation
5	Explainability	Start Simple · Random Forests · Linear Regression

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Script v1.7 (2026-07-28) · FGN