DQ 2

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School

Grand Canyon University *

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Course

MIS 655

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Industrial Engineering

Date

Apr 26, 2024

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docx

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2

Uploaded by ProfField9531 on coursehero.com

Topic 3 DQ 2 Apr 18-22, 2024 What are the key differences between supervised and unsupervised tasks? When is each most appropriate to use? Provide specific examples to support your answers. According to Google (n.d.), the two main methods in artificial intelligence and machine learning are supervised and unsupervised learning. How the models are developed and what kind of training data the algorithms utilize are the easiest ways to distinguish between supervised and unsupervised learning. The usage of labeled datasets is the primary difference between the two methodologies. An unsupervised learning algorithm does not employ labeled input and output data, whereas supervised learning does. (IBM, n.d.). Models for supervised learning have a baseline knowledge of what the appropriate values for the outputs should be. In supervised learning, an algorithm learns to make predictions on a sample dataset and then iteratively fine-tunes its operation to reduce error. These context-labeled datasets supply the intended output values, allowing a model to produce a "correct" response. For example, a supervised model might be used to forecast flight timings depending on a variety of criteria, including weather, airport traffic, peak flight periods, and more. Unsupervised learning algorithms, on the other hand, operate autonomously to discover the underlying structure of the data. For example, unsupervised learning models might be used to find buyer groups who jointly purchase relevant products to promote additional products to comparable consumers. Selecting the best strategy will rely on your team's general approach to data analysis, processing, and management, your overall goals and needs, and the use cases you want to resolve. Datasets with labels are necessary for supervised learning. You must evaluate if your business has the required time, resources, and skills to verify and label data. It's important to think about the problem you're attempting to address and whether you want to develop a prediction model, uncover novel information, or find hidden patterns in the data. It's necessary to consider whether algorithms can support your chosen strategy when determining which one is ideal for your company. (Google, n.d.). References Google. (n.d.). Supervised vs. Unsupervised Learning: What’s the Difference? Retrieved on April 22, 2024, from https://cloud.google.com/discover/supervised-vs-unsupervised- learning#:~:text=The%20biggest%20difference%20between%20supervised,correct%20output %20values%20should%20be .
IBM. (n.d.). Supervised vs. Unsupervised Learning. What’s the Difference? Retrieved on April 22, 2024, from https://www.ibm.com/blog/supervised-vs-unsupervised-learning/
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