CompTIA
DY0-001 · Question #52
Which of the following is a key difference between KNN and k-means machine-learning techniques?
The correct answer is D. KNN is used for classification, while k-means is used for clustering. KNN is a supervised algorithm that assigns labels based on the closest labeled examples, whereas k- means is an unsupervised method that partitions data into clusters by finding centroids without using any pre-existing labels.
Machine Learning
Question
Which of the following is a key difference between KNN and k-means machine-learning techniques?
Options
- AKNN operates exclusively on continuous data, while k-means can work with both continuous and
- BKNN performs better with longitudinal data sets, while k-means performs better with survey data
- CKNN is used for finding centroids, while k-means is used for finding nearest neighbors.
- DKNN is used for classification, while k-means is used for clustering.
How the community answered
(54 responses)- A2% (1)
- B4% (2)
- C2% (1)
- D93% (50)
Explanation
KNN is a supervised algorithm that assigns labels based on the closest labeled examples, whereas k- means is an unsupervised method that partitions data into clusters by finding centroids without using any pre-existing labels.
Topics
#KNN#k-means#classification#clustering
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