A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]
Considering the graph, what is a reasonable selection for the optimal choice of k?
A . 1
B . 4
C . 7
D . 10
Answer: B
Explanation:
The elbow method is a technique that we use to determine the number of centroids (k) to use in a k-means clustering algorithm. In this method, we plot the within-cluster sum of squares (WCSS) against the number of clusters (k) and look for the point where the curve bends sharply. This point is called the elbow point and it indicates that adding more clusters does not improve the model significantly. The graph in the question shows that the elbow point is at k = 4, which means that 4 is a reasonable choice for the optimal number of clusters.
References:
Elbow Method for optimal value of k in KMeans: A tutorial on how to use the elbow method with Amazon SageMaker.
K-Means Clustering: A video that explains the concept and benefits of k-means clustering.
Latest MLS-C01 Dumps Valid Version with 104 Q&As
Latest And Valid Q&A | Instant Download | Once Fail, Full Refund