KNN Algorithm | Learn About Artificial Intelligence
The k-Nearest Neighbors (KNN) algorithm is a simple, versatile, and popular machine learning method used for both classification and regression tasks, making predictions based on the proximity of data points to their nearest neighbors in a dataset.
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KNN is a supervised learning algorithm, meaning it learns from labeled data to make predictions on new, unseen data. KNN relies on a distance metric.
Lazy Learning: It's considered a "lazy learner" because it doesn't have a dedicated training phase; instead, it stores the training data and uses it directly for prediction.
Proximity-Based: KNN relies on the principle that similar data points are located near each other, and it makes predictions based on the classes or values of the nearest neighbors.
Classification: In classification, KNN assigns a new data point to the class that is most common among its k nearest neighbors.
Regression: In regression, KNN predicts a value by averaging the values of the k nearest neighbors.
Parameter k: The parameter 'k' determines the number of nearest neighbors to consider when making a prediction.
