Understand the key differences between supervised learning, unsupervised learning and reinforcement learning, and which ones require human input. Expert-written answer to help you prepare for the IBM Artificial Intelligence Fundamentals certification exam.
Table of Contents
Question
Which of the following statements is correct?
A. Supervised learning requires no human input. except for the raw data itself.
B. Unsupervised learning requires human input except on the raw data.
C. Supervised learning requires humans to provide labeled data.
D. Reinforcement learning is classified by humans.
Answer
C. Supervised learning requires humans to provide labeled data.
Explanation
When humans pre-sort and classify data before inputting it into an AI system, they’re preparing the system for supervised learning.
Supervised learning is a type of machine learning that requires a labeled dataset, where the desired output (label) is already known for the input data. Humans must label the training data to specify the correct output for each input.
In contrast:
- Unsupervised learning works on unlabeled data and tries to find hidden patterns or groupings in the data on its own, without labels or human input.
- Reinforcement learning involves an agent learning through trial-and-error interactions with an environment, receiving rewards or penalties, but it does not require explicit labels from humans.
So only supervised learning requires significant human involvement through the labeling of training data. The other learning approaches can work without this human input, making C the correct answer.
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