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.
Overfitting and generalisation
Overfitting: when memorising beats learning A model can score brilliantly on the data it trained on and still be useless in the real world. That gap is the central problem of machine learning, and it has a name: overfitting. Memorising versus generalising The goal of a supervised model is to generalise — to perform well […]
Cross-validation and the split you got wrong
One split can be lucky. Several cannot all be Hold out 20% of the data, score on it, and you get a number. Do it again with a different 20% and you get a different number — sometimes markedly different, especially on smaller datasets. Neither is wrong; both are one sample of the model’s behaviour. […]