Ch 8 · Overfitting & the Bias-Variance Tradeoff · Machine Learning & AI

Topic 8 of 13 in Machine Learning & AI — Foundations — 2 lessons.

Memorising vs Learning

A model overfits when it memorises the training data — including its noise — and then flops on new data. The tell-tale sign: near-perfect training score, poor test score.

The Bias-Variance Tradeoff

The goal is the sweet spot between them. Tools: more data, simpler models, and regularisation (penalising complexity).

All topics in Machine Learning & AI Beginner

1. What Is Machine Learning?
2. Data — Features, Labels & Splitting
3. Linear Regression — Fitting a Line
4. How Models Learn — Gradient Descent
5. Classification — Predicting Categories
6. Decision Trees
7. Evaluating a Model Honestly
8. Overfitting & the Bias-Variance Tradeoff
9. The ML Workflow
10. Mini-Project — A Spam Classifier
11. Clustering — Finding Groups Without Labels
12. Your First Real scikit-learn Model
13. Words as Numbers — A Taste of NLP