The Ultimate Unsupervised Learning Quiz: Are You Ready?

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| By Madhurima Kashyap
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Madhurima Kashyap
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Quizzes Created: 39 | Total Attempts: 11,368
| Attempts: 181
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  • 1/10 Questions

    What is Unsupervised learning?

    • Training on labeled data
    • Training on unlabeled data
    • A type of reinforcement learning
    • A type of deep learning
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About This Quiz

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.

The Ultimate Unsupervised Learning Quiz: Are You Ready? - Quiz

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  • 2. 

    Which of the following is a type of unsupervised learning?

    • Regression

    • Classification

    • Clustering

    • Convolutional Neural Network

    Correct Answer
    A. Clustering
    Explanation
    Clustering, where the algorithm learns to group similar data, is a type of unsupervised learning.

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  • 3. 

    Which algorithm is commonly used for clustering in unsupervised learning?

    • Naive Bayes

    • Decision Trees

    • Random Forests

    • K-Means

    Correct Answer
    A. K-Means
    Explanation
    This algorithm tries to minimize the variance of data points within a cluster.

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  • 4. 

    What does the DBSCAN algorithm do in unsupervised learning?

    • Classify data

    • Density based clustering algorithm

    • Predict future data

    • Generate new data

    Correct Answer
    A. Density based clustering algorithm
    Explanation
    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.

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  • 5. 

    Which of the following best describes the term 'anomaly detection' in unsupervised learning?

    • Identifying outliers in data

    • Identifying the center of data clusters

    • Classifying data

    • Generating new data samples

    Correct Answer
    A. Identifying outliers in data
    Explanation
    Anomaly detection is the identification of rare events, items, or observations which are suspicious because they differ significantly from standard behaviors or patterns.

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  • 6. 

    Which algorithm is used for hierarchical clustering in unsupervised learning?

    • Naive Bayes

    • Decision Trees

    • Random Forests

    • Agglomerative clustering

    Correct Answer
    A. Agglomerative clustering
    Explanation
    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.

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  • 7. 

    What is the goal of dimensionality reduction in unsupervised learning?

    • To reduce computational complexity

    • To reduce overfitting

    • To improve data visualization

    • All of the above

    Correct Answer
    A. All of the above
    Explanation
    The goal of dimensionality reduction is to reduce the number of dimensions in a way that the new data remains useful.

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  • 8. 

    How are the clusters formed in the K-Means algorithm?

    • Based on nearest neighbors

    • Based on decision tree splitting

    • Based on density

    • Based on distance from centroids

    Correct Answer
    A. Based on distance from centroids
    Explanation
    The classification into clusters is done using criteria such as smallest distances, density of data points, graphs, or various statistical distributions.

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  • 9. 

    Which of the following is a drawback of K-Means clustering?

    • It requires the number of clusters to be known.

    • It can only handle numeric data.

    • It is sensitive to outliers.

    • All of the above.

    Correct Answer
    A. 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.

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  • 10. 

    In the context of unsupervised learning, what is an autoencoder used for?

    • For classification tasks

    • For clustering tasks

    • For dimensionality reduction

    • For time-series prediction

    Correct Answer
    A. For dimensionality reduction
    Explanation
    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.

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  • Current Version
  • Aug 03, 2023
    Quiz Edited by
    ProProfs Editorial Team
  • Aug 02, 2023
    Quiz Created by
    Madhurima Kashyap
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