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AI-900: Understanding Model Labeling Crucial Insights for AI Modeling

Explore the necessity of numeric labels in regression and classification models, unraveling the role of labeling in effective AI model training and accuracy.

Table of Contents

Question

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

Statement 1: For a regression model, labels must be numeric.
Statement 2: For a clustering model, labels must be used.
Statement 3: For a classification model, labels must be numeric.

Answer

Statement 1: For a regression model, labels must be numeric: Yes
Statement 2: For a clustering model, labels must be used: No
Statement 3: For a classification model, labels must be numeric: No

Explanation

Box 1: Yes -For regression problems, the label column must contain numeric data that represents the response variable. Ideally the numeric data represents a continuous scale.
Box 2: No -K-Means Clustering -Because the K-means algorithm is an unsupervised learning method, a label column is optional. If your data includes a label, you can use the label values to guide selection of the clusters and optimize the model. If your data has no label, the algorithm creates clusters representing possible categories, based solely on the data.
Box 3: No -For classification problems, the label column must contain either categorical values or discrete values. Some examples might be a yes/no rating, a disease classification code or name, or an income group. If you pick a noncategorical column, the component will return an error during training.

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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