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Why is regression used to predict car MPG in machine learning?
Master AI-900 concepts by learning why regression is the correct machine learning model for predicting a car’s miles per gallon (MPG). Understand how this supervised learning technique uses quantifiable factors like weight and engine power to forecast a continuous numerical value, and see how it differs from classification and anomaly detection.
Question
For a car, predicting the miles per gallon based on weight, engine power, and other quantifiable factors is an example of ________________.
A. Regression
B. Anomaly detection
C. Classification
D. Clustering
Answer
A. Regression
Explanation
The correct answer is A. Regression. This task is a classic example of a regression problem because it involves predicting a continuous numerical value.
Understanding Regression
Regression is a type of supervised machine learning where the goal is to predict a specific, continuous quantity. In this scenario, “miles per gallon” (MPG) is a continuous numerical value that can fall anywhere within a range. The model is trained on a dataset of cars where the features (weight, engine power, etc.) and the actual MPG are known. The algorithm learns the mathematical relationship between these input features and the MPG outcome. Once trained, the model can take the features of a new car and predict its expected MPG.
Why Other Options Are Incorrect
- Anomaly detection: This workload is used to identify unusual or unexpected data points. For example, it could flag a car that has a drastically lower MPG than other similar models, suggesting a potential mechanical issue. It does not predict the standard MPG value.
- Classification: This is a supervised learning technique used to predict a discrete category or class. If the goal were to label a car as “fuel-efficient,” “average,” or “inefficient” based on its features, classification would be the correct approach. However, since the goal is to predict the specific numerical MPG value, regression is the right choice.
- Clustering: This is an unsupervised learning technique used to group similar items together based on their features, without any predefined labels. For example, clustering could be used to group cars into market segments like “compact sedans,” “SUVs,” or “sports cars” based on their specifications. It does not predict a specific value for an individual car.
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.