Machine Learning Styles (Supervised vs Unsupervised)

Context: FIT1043_MOC · how Machine Learning algorithms learn · split by whether the data is labelled · supervised builds predictive models

Quick Revision

• 🎯 Objective: classify an algorithm by its data ➔ supervised (labelled → predict $y$ from $x$) vs unsupervised (unlabelled → find structure).
• 📦 Core Components: supervised → classification + regression | unsupervised → clustering + association.
• ⚡ Key Constraint: the deciding question is simply “is the data labelled?” — that alone picks the style and the task family.

📝 How It Works

1. Supervised

• Setup ➔ all data labelled; learn to predict output $y$ from input $x$ by approximating the mapping $f:x\to y$ well enough to predict $y$ for new $x$.
• Tasks ➔ classification (output a category, e.g. Red/Blue fish) · regression (output a real value, e.g. dollars/weight).
• Algorithms ➔ linear regression (regression); random forest (both); SVM (classification).

2. Unsupervised

• Setup ➔ all data unlabelled; learn the inherent structure / distribution of the input to learn more about it.
• Tasks ➔ clustering (discover natural groupings, e.g. customers by purchasing behaviour) · association (rules describing large parts of the data, e.g. buy X ⇒ also buy Y).
• Algorithms ➔ k-means (clustering); Apriori (association-rule learning).

⚖️ Core Decision Matrix

Style	Data	Tasks	Example algorithms
Supervised	labelled	classification, regression	linear regression, random forest, SVM
Unsupervised	unlabelled	clustering, association	k-means, Apriori

When It Flips: supervised = "learn a mapping to a known answer"; unsupervised = "learn the structure with no answer key" — classification/regression need labels, clustering/association do not.

🧠 Active Recall

Grouping customers by purchasing behaviour vs predicting a house price — which style/task is each, and why?

Answer

• Short answer: Grouping customers = unsupervised clustering (no labels, find structure); predicting price = supervised regression (labelled real-valued target).
• Why: Labelled? ➔ a known target $y$ ⇒ supervised; no target, only structure ⇒ unsupervised.