Supervised and Unsupervised Learning: With Answers and Without
•• 4 min read
Quick takeaway: If you own a labelled answer key, you’re in supervised territory. If you only have raw measurements, you’re exploring unsupervised land. Both are useful, but their maps, tools, and success signs differ.
1 Why this split matters
Picture two classrooms:
- Supervised class: Every exercise sheet comes with the correct answers on the back. Students can check ‑‑> adjust ‑‑> improve quickly.
- Unsupervised class: No answer sheet. Students must spot patterns themselves—“Ah, these six problems look alike.”
Machine‑learning algorithms behave the same way depending on whether you supply the answers (labels).
2 What is supervised learning?
Definition: Learn a rule from labelled pairs so it can predict for a brand‑new .
Typical tasks
| Task type | Examples in life | Popular algorithms (starter set) |
|---|---|---|
| Regression | Forecast tomorrow’s temperature; predict house price | Linear regression, random forest regressor |
| Classification | Email → spam/ham; image → cat/dog | Logistic regression, decision tree, SVM |
Key ingredients
- Labels: ground‑truth answers (prices, categories).
- Loss function: measures wrongness (MSE, cross‑entropy).
- Train / validation / test splits: make sure the model generalises.
When to reach for it
- You care about prediction accuracy.
- Labels are reliable and affordable.
- Model will face similar data in production.
3 What is unsupervised learning?
Definition: Find structure in data that comes with no outcome labels.
Typical tasks
| Goal | Everyday analogue | Common algorithms |
|---|---|---|
| Clustering | Grouping friends by music taste | k‑means, hierarchical clustering |
| Dimensionality reduction | Summarising 1000 features into 2 | PCA, t‑SNE, autoencoders |
| Density estimation | Spotting rare events (anomalies) | Gaussian Mixture Model, Isolation Forest |
Key ingredients
- Similarity notion: distance metric or density idea.
- Validation heuristic: silhouette score, domain sanity check.
- Visualisation: 2‑D plots to see clusters or manifolds.
When to reach for it
- Labels are missing, expensive, or subjective.
- You need exploratory insights: segments, outliers, gist.
- Pre‑processing step before a supervised model (e.g. compress features).
4 Semi‑supervised & friends
Real life isn’t binary. You may own some labels or get feedback over time.
| Flavor | Tiny definition | Quick example |
|---|---|---|
| Semi-supervised | Few labels guide learning on many unlabeled points | Classify rare disease images |
| Self-supervised | Create labels from data itself | Predict masked words in a sentence |
| Reinforcement | Learn via trial‑and‑error rewards | Game‑playing AIs |
5 Workflow contrast
| Step | Supervised | Unsupervised |
|---|---|---|
| Collect data | Gather features and ground‑truth labels | Gather raw features only |
| Pre‑process | Impute, scale, encode | Same, plus choose distance metric if needed |
| Train | Minimise loss on labelled subset | Optimise cluster compactness or variance explained |
| Validate | Hold‑out error (MSE, accuracy) | Internal scores, manual inspection, downstream success |
| Deploy | Predict labels for new data | Assign cluster membership, flag anomalies |
6 Common pitfalls & pro tips
- Pitfall: Forcing unsupervised clustering when obvious business labels already exist.
Tip: If “ground truth” is available, start supervised—accuracy beats guessing patterns. - Pitfall: Blindly trusting clusters without domain check.
Tip: Always plot and sanity‑check results with subject experts. - Pitfall: Leaking labels into feature engineering in supervised tasks (data snooping).
Tip: Keep test data locked away until final evaluation.
7 Choosing which path
Ask these two questions:
- Do I have trustworthy labels?
- Is my main aim prediction or exploration?
If answers are “yes” and “prediction,” go supervised. Otherwise start unsupervised.
8 Looking ahead
| Next in series | What’s inside |
|---|---|
| Estimating | Loss functions, optimisation tricks, cross‑validation |
| Flexibility vs Interpretability | Controlling variance while staying human‑readable |
Key takeaways
- Supervised: learn from examples with answers; outputs a predictor.
- Unsupervised: learn from raw data; outputs patterns or structure.
- Semi‑ and self‑supervised bridge the gap when labels are scarce.
- Check label availability and project goal before coding—saves time.
Next post: the nuts and bolts of actually fitting a model—loss, gradients, and why splitting your data is non‑negotiable.
