AI-900: Applying Regression Models to Make Numeric Predictions

Certain machine learning tasks demand numeric forecasts rather than classifications. Learn when and how to leverage regression algorithms to predict continuous values.

Question

Which type of machine learning should you use to predict the number of gift cards that will be sold next month?

A. classification
B. regression
C. clustering

Answer

B. regression

Explanation

The correct answer is B. regression.

Regression is a type of supervised machine learning that is used to predict numeric values, such as sales, prices, scores, 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.

Classification is another type of supervised machine learning that is used to predict categorical values, such as labels, classes, categories, etc. Classification models learn the relationship between input features and a discrete target variable, and then use this relationship to assign a class to new data.

Clustering is a type of unsupervised machine learning that is used to discover groups of similar data points, without using any labels or target variables. Clustering models learn the structure and patterns in the data, and then use this structure to assign a cluster to new data.

In this scenario, the task is to predict the number of gift cards that will be sold next month, which is a numeric value. Therefore, regression is the most suitable type of machine learning to use.

References

Microsoft Docs > Previous Versions > Module Categories and Descriptions > Machine Learning Modules > Initialize Model > Clustering > Clustering modules

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