Explain K-Nearest Neighbour (K-NN) classification algorithm with the help of a suitable example

The K-Nearest Neighbour (K-NN) classification algorithm is a simple yet powerful technique used in machine learning for both classification and regression tasks. In the context of Artificial Intelligence (AI) and Machine Learning (ML), K-NN is categorized as a supervised learning algorithm. This means that it requires labeled training data to make predictions on unseen data. The core idea behind K-NN is based on the assumption that similar data points tend to exist close to each other in the feature space.

K-NN is an instance-based or lazy learning algorithm. This means that it does not learn an explicit mapping function during the training phase. Instead, it stores the training dataset and delays the computation until a prediction is required. At prediction time, it uses a distance metric (commonly Euclidean distance) to find the ‘K’ closest training examples in the dataset to the input example, and then classifies the input based on the majority class among those K neighbors.

Working of K-NN Algorithm

The K-NN algorithm works as follows:

1. Choose the number of neighbors (K): This is a user-defined constant that specifies how many nearest neighbors will vote in the classification decision.

2. Calculate the distance between the test data point and all training data points: Most commonly, Euclidean distance is used, although other distance measures like Manhattan or Minkowski distance can also be applied depending on the problem.

3. Sort the distances and determine the K nearest neighbors: After computing the distances, sort them in ascending order and select the top K closest points.

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