Async review

Instruction and application

In Progress

Recap core topics:

• Unit 3: Bias-Variance Tradeoff Analysis

Unit 3: Bias-variance tradeoff analysis

In Unit 3, you explored…

• Fundamentals of bias & varianceDefine statistical bias as the systematic error from under-fitting, where overly simple models miss key patterns.

• Define statistical variance as error from over-reacting to training-set noise, causing models to generalise poorly.

• Explain the bias–variance trade-off curve, showing how total error splits into bias, variance, and irreducible noise as complexity changes.

• Evaluate bias and variance impacts on model reliability, interpretability, and business alignment.

• Mitigation & auditing strategies

Mitigate bias by increasing model capacity or dataset size, and control variance through regularisation or ensembling.

• Assess mitigation effectiveness by comparing fairness improvements against any loss in predictive accuracy.
• Implement detection techniques such as cross-validation, regularisation, data augmentation to identify and correct under- and over-fitting.
• Conduct bias–variance–fairness audits on real data to recommend governance-aligned, evidence-based adjustments.

Knowledge check

Let's see how much you can remember about async unit 3.

Try taking the quiz below, remember it's not a test, it's to help you find out if there are any areas you should brush up on.