Podcast
Questions and Answers
What is the most common measure for evaluating K-means clusters?
What is the most common measure for evaluating K-means clusters?
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- Sum of Squared Error (SSE) (correct)
- Mean Absolute Error (MAE)
How is the error calculated for each point in the context of evaluating K-means clusters?
How is the error calculated for each point in the context of evaluating K-means clusters?
- The sum of distances to all clusters
- The distance to the nearest cluster (correct)
- The distance to the farthest cluster
- The average distance to all clusters
What does SSE stand for in the context of evaluating K-means clusters?
What does SSE stand for in the context of evaluating K-means clusters?
- Squared Sum of Errors
- Standard Squared Error
- Sum of Squared Error (correct)
- Sum of Summed Error
What does a general trend indicate about SSE as the number of clusters (K) increases in K-means clustering?
What does a general trend indicate about SSE as the number of clusters (K) increases in K-means clustering?
Why is a lower SSE or higher K not always better in K-means clustering?
Why is a lower SSE or higher K not always better in K-means clustering?
What is the benefit of using the technique of 'Multiple Runs' for solving the initial centroids problem in K-means clustering?
What is the benefit of using the technique of 'Multiple Runs' for solving the initial centroids problem in K-means clustering?
Which approach uses hierarchical clustering to create a dendrogram and then pick initial centroids based on it?
Which approach uses hierarchical clustering to create a dendrogram and then pick initial centroids based on it?
What is the importance of choosing initial centroids in K-means clustering?
What is the importance of choosing initial centroids in K-means clustering?
What is the technique that involves starting with a larger number of initial centroids than the final desired number of clusters, and gradually reducing the number of centroids to K by combining them based on proximity or similarity?
What is the technique that involves starting with a larger number of initial centroids than the final desired number of clusters, and gradually reducing the number of centroids to K by combining them based on proximity or similarity?
What does the Elbow Method help to determine in K-means clustering?
What does the Elbow Method help to determine in K-means clustering?
Flashcards are hidden until you start studying
