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 Read morein 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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Regression
Classification
Clustering
K-Nearest Neighbors (KNN)
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Decision Trees
Support Vector Machines
K-Nearest Neighbors (KNN)
None of the above
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It has low computational cost.
It doesn't require a training phase.
It is resistant to noisy data.
All of the above
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It has high computational cost.
It requires a training phase.
It is resistant to noisy data.
All of the above
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The type of distance metric used
The number of clusters
The number of nearest neighbors
The type of classification task
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By finding the nearest cluster
By finding the nearest K instances
By finding the nearest rule
By finding the nearest hyperplane
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Euclidean distance
Manhattan distance
Cosine distance
All of the above
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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
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Image Recognition
Speech Recognition
Recommender Systems
All of the above
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