Question

AI, Machine Learning, and IT Careers Unit Test 17 of 2017 of 20 Items Question What are the main differences between supervised learning and unsupervised learning within the confines of machine learning? Explain your answer in 3-5 sentences.(4 points) Short Answer Rubric (4 points) Points Awarded Criteria 4 The answer is very clear. The answer is fully supported by details. There may be a few errors in grammar, usage, or mechanics. Errors do not interfere with meaning. 3 The answer is mostly clear. The answer is supported by mostly relevant details. There are a few errors in grammar, usage, or mechanics. Errors do not interfere with meaning. 2 The answer is somewhat clear. Details are weak or not related. There are some errors in grammar, usage, and mechanics. Errors interfere with meaning. 1 The answer is not clear. There are few or no supporting details. There are many errors in grammar, usage, and mechanics. Errors interfere with meaning. 0 The question is not answered. Skip to navigation page 1 of 1

Answer 1

Supervised learning and unsupervised learning are two primary methodologies in machine learning that differ mainly in the type of data available and the outcomes pursued. In supervised learning, algorithms are trained on labeled datasets, meaning that each training example is paired with an output label, allowing the model to learn and make predictions based on input-output relationships. Conversely, unsupervised learning deals with unlabeled data, where the objective is to identify patterns, groupings, or structures within the data without predefined labels. As a result, while supervised learning is often used for tasks such as classification and regression, unsupervised learning is commonly employed in clustering and association tasks.