Supervised Learning
Teaching an AI by showing it labelled examples — inputs paired with the correct answers — so it can predict answers for new inputs.
Reviewed July 10, 2026Stable
When not to use it
- When you have no labels and can't afford to make them. Labelling is the real cost of supervised learning, and it's usually underestimated by an order of magnitude.
- When the thing you're predicting changes faster than you can relabel. A model trained on last year's fraud catches last year's fraud.
- When the rule is known. If a human can write the condition down in a sentence, write the condition down. Don't learn what you already know.
Reach for something else instead
- Rules — cheaper, instant, auditable, and correct for anything with a known decision boundary.
- Unsupervised methods (clustering, anomaly detection) when you want structure found rather than categories assigned.
- Foundation models with few-shot prompting now solve many small classification tasks with no training set at all — worth testing before you commission labels.
This entry is part of a longer guide: What is machine learning?
Read more on the blog
- Supervised vs unsupervised learning: the four typesMachine learning is usually taught as three types: supervised, unsupervised, and reinforcement learning. That map is still useful, but it no longer covers the paradigm that trains almost every modern AI system. Understanding what the fourth type is, and why it broke the old split, explains how AI actually got here.
- How neural networks work: the idea under all of itUnder every transformer, every image generator, every language model, sits one idea: the neural network. Here's how it actually works, neurons, weights, layers, and the simple trick by which it learns from its own mistakes, explained so it finally makes sense.
- AI vs machine learning vs deep learning: the differenceThese three terms are used interchangeably and are not interchangeable. They are nested: deep learning sits inside machine learning, which sits inside artificial intelligence. Knowing which circle you are in tells you what to expect about data, cost, transparency, and how the system will fail.
- What is reinforcement learning? Learning from rewardReinforcement learning went from a niche corner of AI obsessed with games and robots to the paradigm that shapes how every modern language model behaves. Here's what it actually is, learning by trial, reward, and consequence, why it's different from other machine learning, and how it quietly became the layer between a smart model and a useful one.
Further reading
- Hastie, Tibshirani & Friedman, The Elements of Statistical Learning — still the reference, and free from the authors.
- Kaufman et al. (2012), Leakage in Data Mining — the failure that explains most implausibly good results.
- Sculley et al. (2015), Hidden Technical Debt in Machine Learning Systems — why the model is the small part.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Leaking the answer into the features. If a column is only populated after the outcome, your 99% accuracy is measuring the future, not predicting it.
- Optimising accuracy on imbalanced data. Predict "not fraud" every time and you're 99.9% accurate and completely useless.
- Testing on data that resembles training data more than reality does. The model looks great until launch day.
At a glance
FieldMachine Learning
Core idealearn from labelled examples
Two tasksclassification & regression
Main costgetting labels
DifficultyBeginner
Often compared with
Supervised vs. Unsupervised — learning from labelled answers vs. finding structure in unlabelled data.
Where this sits
1 concept come first. Understanding it opens up 203 more.
1Levelsteps in
1Needs firstconcepts
203Opens up65% of 310
11Areascrosses fields
Learn these firstTraining Data
Computed from the prerequisite graph, not assigned. How this works