Learn why unsupervised learning is the best approach for sorting and categorizing large amounts of uncategorized data, based on a sample question from the IBM AI Fundamentals certification exam.
Table of Contents
Question
Katie has a huge amount of warehouse data to sort but doesn’t know how to categorize it.
Which method of machine learning would help her the most?
A. Reinforcement learning
B. Classical learning
C. Supervised learning
D. Unsupervised learning
Answer
The method of machine learning that would help Katie the most in this scenario is:
D. Unsupervised learning
Explanation
Katie would benefit from using an AI system that uses unsupervised learning because they can structure information by themselves, without human help.
Unsupervised learning is a type of machine learning where the algorithm is trained on a dataset without pre-existing labels or categories. The goal is for the algorithm to discover hidden patterns or groupings in the data on its own. This is in contrast to supervised learning, where the data is already labeled and categorized, and the algorithm learns to map input data to the correct output labels.
In Katie’s case, she has a huge amount of warehouse data that is uncategorized. She doesn’t know in advance what the appropriate categories or groupings should be. This makes it an ideal use case for unsupervised learning.
An unsupervised learning algorithm could process Katie’s warehouse data and automatically sort it into clusters based on inherent similarities that it detects in the data. For example, it might group together products with similar attributes, or records from similar time periods. This could uncover hidden patterns and categorizations that Katie wasn’t aware of. She could then interpret and label the machine-generated clusters to make sense of her data.
Reinforcement learning wouldn’t be applicable here, as it requires a reward signal to learn an optimal action policy through trial-and-error. Classical learning usually refers to simple, pre-deep learning methods like decision trees. And supervised learning requires a pre-labeled training dataset, which Katie doesn’t have. So unsupervised learning is clearly the best fit for this scenario of categorizing a large volume of unlabeled, uncategorized data.
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