Software Automation
K-Nearest Neighbour

What is KNN?

K-Nearest Neighbour (KNN) is a supervised machine learning algorithm used for classification and sometimes regression.

It is based on the idea that “similar things are near each other”. In classification, KNN looks at the k nearest data points (neighbours) to a new sample and uses majority voting to assign a class label.

How Does It Work?

• You have labelled data: features + known labels.

• A new data point arrives. KNN calculates the distance between this new point and all existing data points.

• It picks the k closest points (based on Euclidean distance by default).

• The most common label among those neighbours becomes the predicted class.

Important Parameters

• k (number of neighbours):

  • Small k = very local, sensitive to noise

  • Large k = smoother, more general decision

• Distance metric: Often Euclidean (straight-line), but others like Manhattan (like a grid) can be used.

Is KNN Smart?

Not really. It’s lazy learning:

• No training or model-building phase.

• All the “thinking” happens at the time of prediction.

• This makes KNN simple but computationally expensive for large datasets.