Final answer:
The statements that are NOT true for the nearest neighbor classifier are the ones describing it as partitioning observations into k clusters and computing probabilities using a statistical model. Additionally, a lower value of k does not result in a smoother decision boundary but rather a more complex one.
Step-by-step explanation:
The nearest neighbor classifier is a type of instance-based learning or non-generalizing learning that is used for classification and regression. In this context, several statements about the nearest neighbor classifier have been presented, and we need to identify which ones are NOT true.
- Memorizes the entire training set - This is true for the nearest neighbor classifier; it retains all examples from the training data during the learning phase.
- Partitions observations into k clusters where each observation belongs to the cluster with the nearest mean - This is NOT true because partitioning into clusters is an approach used by k-means clustering, not by the nearest neighbor classifier, which assigns a class based on the closest training example.
- Given a data instance to classify, computes the probability of each possible class using a statistical model of the input features - This is generally NOT true for the basic nearest neighbor classifier, which does not compute probabilities using a statistical model but rather classifies instances based on the closest training example's class.
- A lower value of k generally leads to a smoother decision boundary - This statement is NOT true because a lower value of k in a k-nearest neighbor classifier usually results in a more complex, less smooth decision boundary, as it follows the noise in the training data more closely.