# K-Nearest Neighbors

## Definition of K-Nearest Neighbors

K-Nearest Neighbors (KNN) is a machine learning algorithm used to predict the output value of a target variable by finding the k nearest neighbors of a given input value. The algorithm assigns a weight to each neighbor, then uses a weighted average to predict the output value for the target variable.

## How is K-Nearest Neighbors used?

K-Nearest Neighbors (KNN) is one of the most popular supervised learning algorithms used in data science and machine learning. It is a non-parametric and instance-based algorithm, which means it is based on the relative similarity between instances. KNN works by calculating the distance between a given test instance and all of the training instances, selecting the K nearest training instances, and then making predictions about the test instance based on those K nearest training instances.

The process of classifying a test instance using KNN begins with finding the k-nearest neighbors from among all available training instances. This can be done by using various distance metrics such as Euclidean Distance or Manhattan Distance. Once the closest k neighbors have been identified, their labels are examined to determine which label is most common among them. This label is then attributed to the test instance being classified.

The main advantage of KNN is that it does not make any assumptions about underlying probability distributions or require any prior knowledge about the data set. Additionally, this method can be used for both classification and regression tasks, making it highly versatile and applicable to many different types of data sets. The main disadvantage of KNN is that it can take some time to calculate distances between all points if there are lots of training instances. Additionally, if there are too few training examples in each class, this could lead to overfitting or underfitting – meaning that predictions might not be accurate enough to effectively classify new instances correctly.