AI-900: How to Use Regression Models to Predict Numeric Values

Learn how to use regression models to predict numeric values in AI-900 exam. Find out which scenarios can be solved by regression and which cannot.

Question

Which of the following scenarios can be resolved by using a regression model?

A. Predict selling price of a car using data like engine size, mileage, number of seats etc.
B. Predict daily rental demand of bicycles by using historic data.
C. Predict yearly income of customers based on their occupation, age, education etc.
D. Determine if patients with some pre-existing conditions are more likely to suffer from diabetes.

Answer

A. Predict selling price of a car using data like engine size, mileage, number of seats etc.
B. Predict daily rental demand of bicycles by using historic data.
C. Predict yearly income of customers based on their occupation, age, education etc.

Explanation

Regression is a form of machine learning that is used to predict a numeric label based on an item’s features

A regression model is a type of supervised machine learning that is used to predict numeric values, such as prices, demand, income, etc. Regression models learn the relationship between input features and a continuous target variable, and then use this relationship to make predictions on new data.

The scenarios that can be resolved by using a regression model are A, B, and C, because they all involve predicting a numeric value based on some input features. For example, a regression model can predict the selling price of a car using data like engine size, mileage, number of seats, etc. Similarly, a regression model can predict the daily rental demand of bicycles or the yearly income of customers using historic data and other relevant features.

The scenario that cannot be resolved by using a regression model is D, because it involves determining a binary outcome, such as yes or no, rather than a numeric value. This is a classification problem, not a regression problem. A classification model is another type of supervised machine learning that is used to predict discrete categories, such as labels, classes, or groups. For example, a classification model can determine if patients with some pre-existing conditions are more likely to suffer from diabetes by assigning them a probability of having the disease.

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