Machine Learning

K-Nearest Neighbours

Classifies a point by the labels of the points closest to it. No training step at all.

There is no model to fit — you keep the training data and, at prediction time, find the k closest examples and take a vote. Simple, interpretable, and surprisingly strong on small tabular datasets.

The costs land at inference rather than training, and it degrades badly in high dimensions where everything is roughly equidistant from everything else. Scaling your features is not optional here; distance is the entire method.

Also in Machine Learning

Supervised Learning Unsupervised Learning K-Means Logistic Regression Linear Regression PCA

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