Learn the difference between labels and features in regression scenarios for the Microsoft Azure AI-900 exam. Discover how to identify labels and features in regression models for the AI-900 exam. Master essential machine learning principles with this guide.
Table of Contents
Question
You are designing a regression model to predict sales of cakes based on the time of year, season, and holidays.
In this example, what are the sales and the time of year?
A. Sales are a label
B. Time of year is a label
C. Sales are a feature
D. Time of year is a feature
Answer
A. Sales are a label
D. Time of year is a feature
Explanation
Labels (Output/Dependent Variable)
A label represents the value that the model is tasked with predicting. It is the output or target variable.
In this example, sales are the label because they are the values being predicted based on input features.
Features (Input/Independent Variables)
Features are measurable characteristics or attributes used as inputs to make predictions. They describe or influence the label.
Here, time of year is a feature since it provides input information to predict sales.
Why This Answer is Correct
Labels are the dependent variables in supervised learning tasks, such as regression, where the goal is to predict a continuous value (e.g., cake sales) based on input data.
Features are independent variables that serve as predictors or inputs (e.g., time of year) used by the model to estimate the label126.
By understanding these distinctions, you can effectively design machine learning models and prepare for questions on the AI-900 exam.
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.