Discover the essential features of unsupervised learning in machine learning, including the absence of labeled data and the focus on pattern discovery without supervision.
Table of Contents
Question
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
D. Both inputs as well outputs are specified
Explanation
Unsupervised learning is a branch of machine learning that operates on datasets without labeled outputs. The primary goal is to identify patterns, structures, or groupings within the data without any prior knowledge of the outcomes. This approach contrasts with supervised learning, where models are trained on labeled datasets with known outcomes.
In response to the question regarding which statement is true for unsupervised learning, let’s analyze the provided options:
A. Some specific output values are disclosed: This is incorrect because unsupervised learning does not use labeled outputs; it works with data that has no predefined labels.
B. Some specific output values aren’t disclosed: While this might seem partially correct, it does not accurately capture the essence of unsupervised learning, as it implies that some outputs are known when in fact none are.
C. No relevant inputs value is specified: This statement is misleading. In unsupervised learning, input values are indeed specified; however, they lack corresponding output labels.
D. Both inputs as well outputs are specified: This is incorrect for unsupervised learning because it fundamentally relies on input data without any specified outputs.
Based on this analysis, the correct answer is B. Some specific output values aren’t disclosed. This reflects that while input data is provided for analysis, there are no specific output values associated with it in unsupervised learning scenarios.
In summary, unsupervised learning focuses on discovering hidden patterns within unlabeled data, making it a powerful tool for exploratory data analysis and clustering tasks.
Convolutional Neural Network CNN certification exam assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Convolutional Neural Network CNN exam and earn Convolutional Neural Network CNN certification.