AI Engineering Degree Practice Exam 2026 - Free AI Engineering Practice Questions and Study Guide

In a KNN (k-nearest neighbors) model with k set to 5, the prediction for a new observation is made by taking a majority vote among the 5 nearest neighbors. This method is particularly effective for classification tasks where the response variable represents categories or classes.

When a new observation is introduced, the algorithm identifies the 5 data points in the training set that are closest to this observation based on a chosen distance metric (such as Euclidean distance). Once these neighbors are identified, the algorithm looks at the classes of each of these neighbors. The class that appears most frequently among these 5 points is determined to be the predicted class for the new observation.

This approach capitalizes on the assumption that similar observations are likely to share the same class label, making majority voting a robust and straightforward method to reach a prediction. Consequently, for classification problems, using the majority vote among the closest neighbors is the standard practice, unlike methods that involve averages or medians, which are more suited to regression tasks. Thus, the majority voting mechanism solidly predicts the most appropriate class for the new data point based on the characteristics of its nearest neighbors.