How would you differentiate between K-Means and K-Nearest Neighbors (KNN) algorithms in machine learning?

How would you differentiate between K-Means and K-Nearest Neighbors (KNN) algorithms in machine learning?
A . K-Means requires labeled data to form clusters, whereas KNN does not use labeled data for making predictions
B . K-Means is primarily used for regression tasks, while KNN is used for reducing the dimensionality of data
C . K-Means is a supervised learning algorithm used for classification, while KNN is an unsupervised learning algorithm used for clustering
D . K-Means is an unsupervised learning algorithm used for clustering data points into groups, while KNN is a supervised learning algorithm used for classifying data points based on their proximity to labeled examples

View Answer

Answer: D

Explanation:

Correct option:

K-Means is an unsupervised learning algorithm used for clustering data points into groups, while KNN is a supervised learning algorithm used for classifying data points based on their proximity to labeled examples

K-Means is an unsupervised learning algorithm used to partition a dataset into distinct clusters by minimizing the variance within each cluster. KNN, on the other hand, is a supervised learning algorithm that classifies new data points based on the majority class among its k-nearest neighbors in the training data.

Incorrect options:

K-Means is a supervised learning algorithm used for classification, while KNN is an unsupervised learning algorithm used for clustering – K-Means is an unsupervised learning algorithm, and KNN is a supervised learning algorithm.

K-Means requires labeled data to form clusters, whereas KNN does not use labeled data for making predictions – K-Means does not require labeled data; it is used for clustering. KNN, however, requires labeled data for classification.

K-Means is primarily used for regression tasks, while KNN is used for reducing the dimensionality of data

– K-Means is not used for regression tasks, and KNN is not primarily used for dimensionality reduction. KNN is used for classification and regression tasks based on proximity to neighbors.

References:

https://aws.amazon.com/blogs/machine-learning/k-means-clustering-with-amazon-sagemaker/

https://docs.aws.amazon.com/sagemaker/latest/dg/k-nearest-neighbors.html