Create your own
teaching resources

Sign up for a free account to try! It takes less than 20 seconds

Create free account
Teacherbot 12 Feb, 19:14:59

1.What defines K-means? A. *K-means clustering is an unsupervised learning algorithm used to define clusters of data points B. K-means is a supervised learning algorithm used for pattern recognition C. K-means is a reinforcement learning algorithm used for classification tasks D. K-means is a type of deep neural network

  1. What is the steps involved in K-means clustering? A. Data pre-processing, clustering, interpret results B. Initialization, selection, evaluation, optimization C. *Data initialization, clustering, optimization D. Feature extraction, clustering, interpret results

  2. What is the goal of K-means clustering? A. *To group similar objects together into clusters B. To create an unsupervised learning model C. To classify data points based on inputs D. To reduce the cost of the model

  3. What is one of the disadvantages of K-means clustering? A. It is computationally complex B. *It is not well-suited for data with non-convex clusters C. It is difficult to understand the results of clustering D. It does not scale well for high-dimensional data

  4. How does K-means assign data points to clusters? A. By calculating the distances between clusters B. By predicting the labels of data points C. *By minimizing the Sum of Squared Errors D. By using a Random Forest algorithm

  5. What is the centroid in K-means clustering? A. The smallest cluster within the data B. A randomly chosen seed C. *The mean of a cluster’s points D. The centroid doesn’t have a specific definition

  6. How many iterations are typically used in K-means clustering? A. There is no fixed number of iterations B. 10 C. 1,000 D. *The number of iterations depends on the data

  7. What is the importance of choosing the right number of clusters? A. *The right number of clusters will lead to more accurate results B. It will reduce the running time of the algorithm C. It will improve the interpretability of the results D. It will require less data pre-processing

  8. In what type of problem can K-means clustering be used? A. Regression problems B. Classification problems C. *Clustering problems D. Time series prediction problems

  9. What is the Elbow method used for? A. *Choosing the right number of clusters B. Calculating the Sum of Squared Errors C. Defining cluster centroids D. Creating data pre-processing functions