Which algorithm classifies data points based on the majority label among their K closest neighbors?

The correct choice is K-nearest neighbors, an algorithm that operates on the principle of classifying data points based on the majority label of their K closest neighbors. This method relies on calculating the distance between data points (typically using metrics such as Euclidean distance) to identify which neighbors are most similar. By counting the number of instances of each class among these neighbors, the algorithm can assign the most common label to the new data point.

The strength of the K-nearest neighbors algorithm lies in its simplicity and effectiveness, especially in scenarios where the decision boundary is not linear. It can easily adapt to various datasets as it does not make any assumptions about the underlying data distribution.

Other options represent different classification techniques: K-means is primarily a clustering algorithm used to partition data into distinct groups rather than classifying them; decision trees create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features, while random forests are an ensemble method that builds multiple decision trees and merges their predictions for more robust outcomes. These algorithms do not classify based on the majority label among K neighbors.