The Ultimate Unsupervised Learning Quiz: Are You Ready?
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The Ultimate Unsupervised Learning Quiz: Are You Ready?" is designed to test your understanding of critical unsupervised learning concepts. This 10-question Unsupervised Learning Quiz covers the definition of unsupervised learning, different types of learning techniques, and their applications. Questions include understanding the role of clustering, dimensionality reduction, and algorithms like K-Means and DBSCAN. It also explores how to identify outliers using anomaly detection, hierarchical clustering, and the limitations of K-Means clustering. This quiz comprehensively evaluates your knowledge of the subject, helping identify areas for further study.
- 1.
What is Unsupervised learning?
- A.
Training on labeled data
- B.
Training on unlabeled data
- C.
A type of reinforcement learning
- D.
A type of deep learning
Correct Answer
B. Training on unlabeled dataExplanation
Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention.Rate this question:
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- 2.
Which of the following is a type of unsupervised learning?
- A.
Regression
- B.
Classification
- C.
Clustering
- D.
Convolutional Neural Network
Correct Answer
C. ClusteringExplanation
Clustering, where the algorithm learns to group similar data, is a type of unsupervised learning.Rate this question:
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- 3.
Which algorithm is commonly used for clustering in unsupervised learning?
- A.
Naive Bayes
- B.
Decision Trees
- C.
Random Forests
- D.
K-Means
Correct Answer
D. K-MeansExplanation
This algorithm tries to minimize the variance of data points within a cluster.Rate this question:
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- 4.
What is the goal of dimensionality reduction in unsupervised learning?
- A.
To reduce computational complexity
- B.
To reduce overfitting
- C.
To improve data visualization
- D.
All of the above
Correct Answer
D. All of the aboveExplanation
The goal of dimensionality reduction is to reduce the number of dimensions in a way that the new data remains useful.Rate this question:
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- 5.
What does the DBSCAN algorithm do in unsupervised learning?
- A.
Classify data
- B.
Density based clustering algorithm
- C.
Predict future data
- D.
Generate new data
Correct Answer
B. Density based clustering algorithmExplanation
DBSCAN is a density-based clustering algorithm that works on the assumption that clusters are dense regions in space separated by regions of lower density. It groups 'densely grouped' data points into a single cluster.Rate this question:
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- 6.
In the context of unsupervised learning, what is an autoencoder used for?
- A.
For classification tasks
- B.
For clustering tasks
- C.
For dimensionality reduction
- D.
For time-series prediction
Correct Answer
C. For dimensionality reductionExplanation
Autoencoders are used to help reduce the noise in data. Through the process of compressing input data, encoding it, and then reconstructing it as an output, autoencoders allow you to reduce dimensionality and focus only on areas of real value.Rate this question:
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- 7.
Which of the following best describes the term 'anomaly detection' in unsupervised learning?
- A.
Identifying outliers in data
- B.
Identifying the center of data clusters
- C.
Classifying data
- D.
Generating new data samples
Correct Answer
A. Identifying outliers in dataExplanation
Anomaly detection is the identification of rare events, items, or observations which are suspicious because they differ significantly from standard behaviors or patterns.Rate this question:
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- 8.
Which algorithm is used for hierarchical clustering in unsupervised learning?
- A.
Naive Bayes
- B.
Decision Trees
- C.
Random Forests
- D.
Agglomerative clustering
Correct Answer
D. Agglomerative clusteringExplanation
o group the datasets into clusters, it follows the bottom-up approach. It means, this algorithm considers each dataset as a single cluster at the beginning, and then start combining the closest pair of clusters together.Rate this question:
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- 9.
How are the clusters formed in the K-Means algorithm?
- A.
Based on nearest neighbors
- B.
Based on decision tree splitting
- C.
Based on density
- D.
Based on distance from centroids
Correct Answer
D. Based on distance from centroidsExplanation
The classification into clusters is done using criteria such as smallest distances, density of data points, graphs, or various statistical distributions.Rate this question:
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- 10.
Which of the following is a drawback of K-Means clustering?
- A.
It requires the number of clusters to be known.
- B.
It can only handle numeric data.
- C.
It is sensitive to outliers.
- D.
All of the above.
Correct Answer
D. All of the above.Explanation
It requires to specify the number of clusters (k) in advance. It can not handle noisy data and outliers. It is not suitable to identify clusters with non-convex shapes.Rate this question:
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Current Version
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Aug 03, 2023Quiz Edited by
ProProfs Editorial Team -
Aug 02, 2023Quiz Created by
Madhurima Kashyap
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