K-nearest Neighbour (KNN)

KNN works by measuring the distance between a new data point and the examples in the training dataset. It identifies the K nearest neighbors and uses their information to make a prediction. For classification tasks, the algorithm typically assigns the most common class among the neighbors. For regression tasks, it predicts a value based on the average or weighted average of the neighboring values.

KNN is simple to understand and implement, making it a popular baseline algorithm for machine learning. Since it does not build an explicit model during training, it can adapt to complex decision boundaries. However, its performance depends on selecting an appropriate value of K, choosing a suitable distance metric, and efficiently handling large datasets.