K-Nearest Neighbor (KNN) classification is a simple yet effective machine learning algorithm that can be used for both regression and classification problems. It is a non-parametric and instance-based algorithm that relies on the similarity between data points to make predictions.
The KNN classification algorithm can be summarized in the following steps:
Choose the value of K: The first step in KNN classification is to choose a value of K, which represents the number of nearest neighbors to consider when making a prediction. Typically, K is chosen through trial and error, or by using cross-validation.
Calculate the distance between the test data point and all the training data points: This step involves calculating the distance between the test data point and all the training data points. The most commonly used distance metric is Euclidean distance, but other metrics such as Manhattan distance or cosine similarity can also be used.
Select the K-nearest neighbors: The K-nearest neighbors are selected based on their distance from the test data point. These neighbors serve as the basis for making the prediction.
Assign a class label to the test data point: In this step, the class label of the test data point is assigned based on the majority class label of the K-nearest neighbors. For example, if K=5 and three of the neighbors belong to class A and two belong to class B, the test data point would be assigned to class A.
Repeat steps 2-4 for all test data points: The KNN classification algorithm is applied to all test data points to generate the predictions.
KNN classification has several advantages, such as its simplicity, interpretability, and ability to handle non-linear decision boundaries. However, it also has some limitations, such as its sensitivity to the choice of K and the curse of dimensionality, where the algorithm struggles when there are many features or dimensions.
Overall, KNN classification is a powerful tool for solving classification problems and can be used in a variety of applications such as image classification, text classification, and recommendation systems.