K-Nearest Neighbors (KNN)

What is K-Nearest Neighbors (KNN)? K-Nearest Neighbors (KNN) Explained

K-nearest neighbors (KNN) is a supervised machine learning algorithm used for classification and regression tasks. It is a non-parametric algorithm that makes predictions based on the similarity of data points in the feature space.

In KNN, the “K" represents the number of nearest neighbors used to make a prediction. Given a new data point, the algorithm finds the K closest labeled data points in the training set based on a distance metric (typically Euclidean distance) and assigns the majority class (for classification) or calculates the average (for regression) of the labels of those K neighbors as the prediction for the new data point.

The steps involved in the KNN algorithm are as follows:

Load the training data: Prepare a labeled dataset with input features and corresponding labels.

Choose a distance metric: Select a distance metric, typically Euclidean distance, to measure the similarity between data points in the feature space.

Choose the value of K: Determine the number of nearest neighbors, K, to consider when making predictions. It is important to choose an appropriate value for K, as a low K may result in overfitting while a high K may lead to oversmoothing.

Calculate distances: For a new data point, calculate the distance to all other data points in the training set using the chosen distance metric.

Find K nearest neighbors: Select the K data points with the shortest distances to the new data point.

Make predictions: For classification tasks, assign the class label that appears most frequently among the K nearest neighbors as the prediction for the new data point. For regression tasks, calculate the average of the target values of the K nearest neighbors.

KNN is a lazy learning algorithm because it does not involve explicit training or model building. Instead, it uses the entire training dataset during prediction, making it computationally expensive for large datasets. Additionally, KNN does not make any assumptions about the underlying data distribution, which can be both an advantage and a disadvantage depending on the dataset.

KNN has various applications, including image recognition, document classification, recommendation systems, and anomaly detection. It is relatively easy to understand and implement, making it a popular choice for simple classification and regression tasks.