Which of the following is true for the nearest neighbor classifier (Select all that apply):

Partitions observations into k clusters where each observation belongs to the cluster with the nearest mean
A higher value of k leads to a more complex decision boundary
Given a data instance to classify, computers the probability of each possible class using a statistical model of the input features

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.

i dont know cuh

d and a is the correct anwsers

explanation: