The K-Nearest Neighbors (KNN) algorithm is a fundamental, non-parametric supervised learning method widely used for classification and regression tasks in machine learning. It operates on the principle that similar data points are likely to have similar outcomes.
How KNN Works:
- Training Phase: KNN does not have an explicit training phase. Instead, it stores the entire training dataset, which is used during the prediction phase.
- Prediction Phase:
- Distance Measurement: For a new data point, KNN calculates the distance to all points in the training dataset using a distance metric, such as Euclidean distance.
- Neighbor Selection: It identifies the ‘k’ closest training data points to the new data point.
- Voting (Classification): The algorithm assigns the most common class among these ‘k’ neighbors to the new data point.
- Averaging (Regression): For regression tasks, KNN computes the average of the target values of the ‘k’ nearest neighbors and assigns this value to the new data point.
Applications of KNN:
- Classification Tasks: KNN is commonly used in applications like image recognition, spam detection, and medical diagnosis.
- Regression Tasks: It can also be applied to predict continuous values, such as estimating house prices based on features like size and location.