Discover the key characteristics of unsupervised learning in machine learning.
Table of Contents
Question
Outline out the following option, which of the following is true for unsupervised learning?
A. Some specific output values are disclosed
B. Some specific output values aren’t disclosed
C. No relevant inputs value is specified
D. Both inputs as well outputs are specified
Answer
B. Some specific output values aren’t disclosed
Explanation
Unsupervised learning is a type of machine learning where models are trained using data that is not labeled or categorized. The main goal of unsupervised learning is to identify patterns, groupings, and structures within the data without any explicit guidance.
To address the question regarding which statement is true for unsupervised learning, let’s analyze the provided options:
A. Some specific output values are disclosed: This statement implies that there are known output values, which contradicts the essence of unsupervised learning. In unsupervised learning, no output labels are provided.
B. Some specific output values aren’t disclosed: While this statement is partially true, it does not capture the complete nature of unsupervised learning, as it implies that some outputs might be known, which is not the case.
C. No relevant input values are specified: This statement is incorrect because unsupervised learning does work with input data; it simply does not require labeled outputs.
D. Both inputs and outputs are specified: This statement is false in the context of unsupervised learning since it requires only input data without corresponding output labels.
The correct understanding of unsupervised learning aligns with the notion that it operates on unlabeled data to discover patterns and relationships within the data itself. Thus, the correct answer to the question is:
B. Some specific output values aren’t disclosed
This option accurately reflects that while there may be inputs available, there are no corresponding outputs provided in unsupervised learning scenarios.
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