K-Nearest Neighbors (KNN) Algorithm The KNN algorithm is a non-parametric machine learning algorithm used for both supervised and unsupervised learning. It...
K-Nearest Neighbors (KNN) Algorithm
The KNN algorithm is a non-parametric machine learning algorithm used for both supervised and unsupervised learning. It operates by considering a set of labeled training examples (called the "k nearest neighbors" to the new data point) and identifying the class that is most frequent among them.
Distance Metrics
The distance metric determines how similar or different two data points are. Common metrics include:
Euclidean distance: The distance between two points is calculated by summing the square differences between their corresponding features.
Manhattan distance: The distance between two points is calculated by summing the absolute differences between their corresponding features.
Cosine distance: The cosine distance measures the angle between two vectors. A small angle indicates similar vectors, while a large angle indicates different vectors.
Dynamic time warping (DTW): This metric considers the shape of the data in higher dimensions by comparing the distances between corresponding points in different feature spaces.
Examples
K-Nearest Neighbors with Euclidean Distance: Suppose we have a dataset of labeled customers and their purchase records. We can use the Euclidean distance to calculate the distance between a new customer's record and each customer in the dataset. The customer with the smallest distance is assigned to the same class as the new customer.
K-Nearest Neighbors with Manhattan Distance: If we have a dataset of customers with different names but similar purchase patterns, we can use the Manhattan distance to calculate the distance between their records.
K-Nearest Neighbors with Cosine Distance: If we have a dataset of songs and we want to find similar songs, we can use the cosine distance to calculate the angle between the vectors representing the songs. The songs with the smallest angle are considered similar