Supervised Learning Essentials
A practical tour of regression, classification, and the evaluation metrics — accuracy, precision, recall, and overfitting checks — that reveal whether a model truly works.
Predicting categories: classification
Classification predicts a category The second flavour of supervised learning is classification: instead of a number, the model predicts a class or label. Is this email spam or not spam? Will this customer churn or stay? Which of five product categories does this item belong to? The answer is a bucket, not a value on […]
Feature engineering, plainly
The model sees only what you put in front of it A supervised model does not understand your business. It sees columns of numbers. Feature engineering is the work of turning what you know into columns the model can use — and on most real problems it moves the result more than swapping algorithms ever […]
Choosing the threshold is a business decision
Most classifiers do not output a class. They output a probability A fraud model does not say “fraud”. It says 0.73. Something then has to decide that 0.73 counts as fraud — and that something is a threshold you choose, not a fact the model discovered. Almost everyone leaves it at 0.5 because that is […]
A model card for the model you built
The document that makes a model safe to hand over A trained model on its own is an artefact nobody else can judge. A model card is the short, honest document that travels with it — what it does, on whom, how well, and where it should not be used. The practice comes out of […]