adplus-dvertising
frame-decoration

Question

What is a common goal of unsupervised learning?

a.

Dimensionality reduction

b.

Classification

c.

Clustering

d.

Regression analysis

Posted under Big Data Computing

Answer: (c).Clustering Explanation:A common goal of unsupervised learning is clustering, where data instances are grouped together based on similarity.

Engage with the Community - Add Your Comment

Confused About the Answer? Ask for Details Here.

Know the Explanation? Add it Here.

Q. What is a common goal of unsupervised learning?

Similar Questions

Discover Related MCQs

Q. Why is machine learning well-suited for handling Big Data?

Q. What types of tasks are strengths of machine learning algorithms in the context of Big Data?

Q. What is the primary purpose of decision tree learning?

Q. How are decision tree algorithms like ID3 and C4.5 used in the learning process?

Q. What measures are used to determine the selection of attributes for splitting in decision trees?

Q. What happens when an instance is incorrectly classified in the decision tree learning process?

Q. What is one of the improved versions of the ID3 decision tree algorithm?

Q. What does the "rxDTree" function in Revolution R* Enterprise 6.1 software provide for decision tree learning?

Q. How does data parallelism work in the context of Big Data decision trees?

Q. What is the potential drawback of using the "rxDTree" function for decision tree construction?

Q. What are decision trees converted into to improve readability?

Q. What is one way to obtain a more readable representation of a decision tree?

Q. What is the covering approach in rule induction?

Q. What is the main difference between clustering and classification?

Q. How are clusters defined in clustering?

Q. Which metric is commonly used to calculate distances in the k-means clustering algorithm?

Q. What is the challenge associated with the k-means algorithm?

Q. How can the challenge associated with the k-means algorithm be mitigated?

Q. Which category of clustering algorithms creates a nested set of clusters?

Q. What is the primary difference between agglomerative and divisive hierarchical clustering?