Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, How to find optimal k value in kmeans? Elbow method vs silhouette method, Which is the best method to find optimal number of clusters in kmeans?
Machine Learning MCQ  Which is the method to find optimal number of clusters (k value) in kmeans algorithm?
1. Kmeans is an unsupervised learning algorithm. In Kmeans, k refers to the number of clusters. We have several methods to find the optimal number of clusters in Kmeans algorithm. Which of the following methods can give optimal (best) number of clusters?
a) Manhattan method
b) Elbow method
c) Euclidean method
d) Silhouette method
Answer: (d) Silhouette method The silhouette method for finding optimal k value in kmeansThe silhouette Method is a method to find the optimal number of clusters and interpretation and validation of consistency within clusters of data. The silhouette method computes silhouette coefficients of each point that measure how much a point is similar to its own cluster compared to other clusters by providing a succinct graphical representation of how well each object has been classified. Compute silhouette coefficients for each of point, and average it out for all the samples to get the silhouette score. [For more please refer here]
Why elbow method is not chosen as the better method over silhouette method in finding the best value for k in kmeans?Usually elbow curve method is a little ambiguous as the bend point for some datasets is not visible clearly.
Difference between Elbow and Silhouette methods

No comments:
Post a Comment