Answer:
1. Partitions observations into K cluster where each observation belongs to the cluster.
Explanation:
Near neighbor classifier is a non-parametric method used for classification and regression. It is a method of supervised statistical a pattern in the study of population.
It allocates each observation in clusters to make observation very easy. It achieves a high performance rate. A sample is said to be classified by calculating the nearest distance to the training case. It involves both the positive abd negative case of training case.
The nearest neighbor classifier is to partition observations into K cluster, where each observation belongs to the cluster. Option A is correct.
A machine learning method called nearest neighbor classification seeks to identify previously unseen query objects while distinguishing two or more destination classes.
It, like any other classifier, requires some training data with predetermined labels and is thus an example of supervised learning.
It is a non-parametric method for classification and regression is the near neighbor classifier. It is a guided statistical pattern in population research method.
Furthermore, it divides observations into K clusters, with each observation belonging to one of the clusters.
Therefore, option A correct.
Learn more about the neighbor classifier, refer to:
https://brainly.com/question/316268
