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AI-900: How to Use Regression Models to Predict Auction Prices

Learn how to use regression models to predict the sale price of auctioned items and ace the AI-900 exam with this comprehensive guide.


To complete the sentence, select the appropriate option in the answer

__________ models can be used to predict the scale price of auctioned items.

A. Classification
B. Clustering
C. Regression


C. Regression


Regression is a machine learning task that is used to predict the value of the label from a set of related features.

The correct answer is C. Regression. Regression models are used to predict a continuous numerical value, such as the sale price of an item. Classification models are used to predict a discrete category, such as the type of an item. Clustering models are used to group similar items together, such as the customers of an online store.

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump

Alex Lim is a certified IT Technical Support Architect with over 15 years of experience in designing, implementing, and troubleshooting complex IT systems and networks. He has worked for leading IT companies, such as Microsoft, IBM, and Cisco, providing technical support and solutions to clients across various industries and sectors. Alex has a bachelor’s degree in computer science from the National University of Singapore and a master’s degree in information security from the Massachusetts Institute of Technology. He is also the author of several best-selling books on IT technical support, such as The IT Technical Support Handbook and Troubleshooting IT Systems and Networks. Alex lives in Bandar, Johore, Malaysia with his wife and two chilrdren. You can reach him at [email protected] or follow him on Website | Twitter | Facebook

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