AWS Certified AI Practitioner · Free Practice Question Medium
Question 24
A media company is developing a machine learning model to categorize its vast library of content. The data science team is trying to decide between using multi-class or multi-label classification based on the complexity of the content categories. Understanding the differences between multi-class and multi-label classification will help the team choose the most appropriate approach for organizing their content effectively.
What do you recommend to the company?
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A
Multi-class classification assigns each instance to one of several possible classes, while multi-label classification assigns each instance to one or more classes
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B
Multi-class classification is used exclusively for image data, whereas multi-label classification is used exclusively for text data
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C
Multi-class classification allows each instance to belong to multiple classes simultaneously, whereas multi-label classification restricts each instance to one class only
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D
Multi-class classification does not require labeled data, whereas multi-label classification requires labeled data for training
Reveal correct answer
Correct answer: A
Explanation
Correct option:
Multi-class classification assigns each instance to one of several possible classes, while multi-label classification assigns each instance to one or more classes
With multi-label classification, you can train models and classify your documents with more than one label. For example, you can use multi-label classification to categorize customer contact transcripts with one or more labels to identify departments within your company like Payments, Renewals, or Tech Support. These labels can then be mapped to relevant content in your support library or directed towards the appropriate contacts within your company. ML models for multiclass classification problems allow you to generate predictions for multiple classes (predict one of more than two outcomes).
Therefore, the key difference is that multi-class classification assigns each instance to one of several possible classes (e.g., an image classified as either a cat, dog, or bird), whereas multi-label classification can assign each instance to multiple classes simultaneously (e.g., a document classified as both "science" and "technology").
Incorrect options:
Multi-class classification allows each instance to belong to multiple classes simultaneously, whereas multi-label classification restricts each instance to one class only - Multi-class classification restricts each instance to one class, while multi-label classification allows each instance to belong to multiple classes. So, this option is incorrect.
Multi-class classification is used exclusively for image data, whereas multi-label classification is used exclusively for text data - Both multi-class and multi-label classification can be applied to various data types, including images and text.
Multi-class classification does not require labeled data, whereas multi-label classification requires labeled data for training - Both types of classification require labeled data for training the models effectively.
References:
https://docs.aws.amazon.com/machine-learning/latest/dg/types-of-ml-models.html
Discussion
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