Final answer:
K-NN, or K-Nearest Neighbors, is a machine learning algorithm that predicts the category of an object by examining the categories of its closest neighbors in a dataset.
Step-by-step explanation:
K-NN, or K-Nearest Neighbors, is a type of instance-based machine learning algorithm used to categorize objects based on their feature similarity with objects in a dataset. You are asked to identify what is X using the K-NN algorithm given two scenarios: when K=3 and when K=5. You must identify the category of an uncategorized point X given K=3 or K=5, using the nearest neighbors to predict its category.
The 'given' information typically includes a dataset with known categorized points and the features of an uncategorized point X whose category we need to predict. We 'find' the category of X by looking at the categories of the K-nearest neighbors in the dataset.
For K=3, we find the three data points in the dataset that are closest to X based on a chosen distance metric (such as Euclidean distance), and assign X the most frequent category among these three neighbors. For K=5, the process is similar but involves finding the five closest neighbors.