The Instance-Based Learning Quiz: Are You an Expert?

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

    What is instance-based learning?

    • Learning based on general rules
    • Learning based on specific instances
    • A type of reinforcement learning
    • A type of unsupervised learning
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About This Quiz

The Instance-Based Learning Quiz: Are You an Expert?" is a meticulously crafted questionnaire that evaluates your knowledge about instance-based learning. Instance-Based Learning Quiz delves into the fundamental nature of instance-based learning, standard algorithms such as K-Nearest Neighbors (KNN), and how it works.
It helps assess your understanding of key advantages and disadvantages, particularly computational costs and the absence of a training phase. The quiz further scrutinizes your grasp on elements like 'K' in KNN, decision-making processes, distance metrics, and decision boundaries' behavior with varying 'K' values. Concluding with real-world applications of KNN, this quiz presents a comprehensive check on your proficiency in instance-based learning.

The Instance-based Learning Quiz: Are You An Expert? - Quiz

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  • 2. 

    Which of the following is a type of instance-based learning?

    • Regression

    • Classification

    • Clustering

    • K-Nearest Neighbors (KNN)

    Correct Answer
    A. K-Nearest Neighbors (KNN)
    Explanation
    K-Nearest Neighbors (KNN) is a type of instance-based learning where the classification of a point is based on the points that are nearest to it.

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  • 3. 

    Which algorithm is commonly used in instance-based learning?

    • Decision Trees

    • Support Vector Machines

    • K-Nearest Neighbors (KNN)

    • None of the above

    Correct Answer
    A. K-Nearest Neighbors (KNN)
    Explanation
    KNN is a non-parametric algorithm because it does not assume any specific form or underlying structure in the data. Instead, it relies on a measure of similarity between each pair of data points.

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  • 4. 

    What is the main disadvantage of instance-based learning?

    • It has high computational cost.

    • It requires a training phase.

    • It is resistant to noisy data.

    • All of the above

    Correct Answer
    A. It has high computational cost.
    Explanation
    The main disadvantage of instance-based learning is that it has high computational cost as it needs to compute the distance to all training points.

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  • 5. 

    In the context of instance-based learning, what is K in K-Nearest Neighbors (KNN)?

    • The type of distance metric used

    • The number of clusters

    • The number of nearest neighbors

    • The type of classification task

    Correct Answer
    A. The number of nearest neighbors
    Explanation
    The k value in the k-NN algorithm defines how many neighbors will be checked to determine the classification of a specific query point.

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  • 6. 

    How does K-Nearest Neighbors (KNN) make predictions?

    • By finding the nearest cluster

    • By finding the nearest K instances

    • By finding the nearest rule

    • By finding the nearest hyperplane

    Correct Answer
    A. By finding the nearest K instances
    Explanation
    The KNN algorithm uses a majority voting mechanism. It collects data from a training data set, and uses this data later to make predictions for new records. For each new record, the k-closest records of the training data set are determined.

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  • 7. 

    Which of the following is a real-world application of K-Nearest Neighbors (KNN)?

    • Image Recognition

    • Speech Recognition

    • Recommender Systems

    • All of the above

    Correct Answer
    A. Recommender Systems
    Explanation
    KNN can be used for Recommendation Systems. Although in the real world, more sophisticated algorithms are used for the recommendation system. KNN is not suitable for high dimensional data, but KNN is an excellent baseline approach for the systems.

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  • 8. 

    What is the main advantage of instance-based learning?

    • It has low computational cost.

    • It doesn't require a training phase.

    • It is resistant to noisy data.

    • All of the above

    Correct Answer
    A. It doesn't require a training phase.
    Explanation
    One main advantage of instance-based learning is that it doesn't require a training phase as it uses the training instances for prediction.

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  • 9. 

    Which distance metric is commonly used in K-Nearest Neighbors (KNN)?

    • Euclidean distance

    • Manhattan distance

    • Cosine distance

    • All of the above

    Correct Answer
    A. Euclidean distance
    Explanation
    This distance is the most widely used one as it is the default metric that SKlearn library of Python uses for K-Nearest Neighbour. It is a measure of the true straight line distance between two points in Euclidean space.

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  • 10. 

    In K-Nearest Neighbors (KNN), how does changing the value of K affect the decision boundary?

    • Larger K makes the decision boundary smoother

    • Smaller K makes the decision boundary smoother

    • K does not affect the decision boundary

    • None of the above

    Correct Answer
    A. Larger K makes the decision boundary smoother
    Explanation
    In K-Nearest Neighbors (KNN), larger values of K generally result in smoother decision boundaries, reducing the chance of overfitting.

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Quiz Review Timeline (Updated): Aug 3, 2023 +

Our quizzes are rigorously reviewed, monitored and continuously updated by our expert board to maintain accuracy, relevance, and timeliness.

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