DRAG DROP -Match the types of machine learning to the appropriate scenarios. To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
- Predict how many minutes late a flight will arrive based on the amount of snowfall at an airport.
- Segment customers into different groups to support a marketing department.
- Predict whether a student will complete a university course.
- Regression: Predict how many minutes late a flight will arrive based on the amount of snowfall at an airport.
- Clustering: Segment customers into different groups to support a marketing department.
- Classification: Predict whether a student will complete a university course.
Box 1: Regression -In the most basic sense, regression refers to prediction of a numeric target. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Box 2: Clustering -Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation. Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.
Box 3: Classification -Two-class classification provides the answer to simple two-choice questions such as Yes/No or True/False.
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