Learn how regression models can predict used car prices based on make, model, engine size and mileage. Discover why regression is ideal for this machine learning task.
Table of Contents
Question
An automobile company wants to use historic car sales data to train a machine learning model. The model should predict the price of a pre-owned car based on its make, model, engine size, and mileage. What kind of machine learning model should the dealership use automated machine learning to create?
A. Clustering.
B. Classification.
C. Regression.
Answer
C. Regression.
Explanation
The automobile company should use automated machine learning to create a regression model to predict the price of a pre-owned car based on its features like make, model, engine size, and mileage.
Regression models are used for predicting a continuous numerical value, such as a price, based on one or more input features. In this case, the target variable is the price of the used car, which is a continuous dollar amount. The input features like make, model, engine size and mileage will be used to predict this price.
Regression algorithms like linear regression, decision tree regressors, or ensemble methods find patterns in historical data to create a mathematical model mapping the input features to the target price. The model learns the relationship between car features and their associated prices from the training data. Then, given a new car’s features, the trained regression model can predict its likely price.
Other model types like classification and clustering would not be appropriate here:
- Classification predicts which category an input belongs to, not a continuous value
- Clustering finds similar groups in data but doesn’t predict a target variable
Therefore, regression is the correct type of machine learning model for the dealership to automatically build in order to predict used car prices from vehicle attributes. The regression model will learn from past sales data to estimate prices of pre-owned cars based on their specific characteristics.
The dealership should use a regression model because it aims to predict a continuous numerical value, which in this case is the price of a pre-owned car. Regression models are suitable for scenarios where the output is a numeric value based on input features, such as make, model, engine size, and mileage.
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