Learn how to use regression for traffic prediction with Azure Machine Learning. Find out what regression is and how it can help you predict how many vehicles will travel across a bridge on a given day.
Table of Contents
Question
HOTSPOT (Drag & Drop is not supported)
Select the answer that correctly completes the sentence.
Predicting how many vehicles will travel across a bridge on a given day is an example of __________.
A. classification
B. clustering
C. regression
Answer
C. regression
Explanation
Regression is a machine learning task that is used to predict the value of the label from a set of related features.
The correct answer is C. regression. Regression is a type of machine learning technique that predicts a continuous numerical value based on the input features. For example, predicting how many vehicles will travel across a bridge on a given day is a regression problem, because the output is a numerical value that can vary within a range. Regression models can use different algorithms to learn the relationship between the input features and the output value, such as linear regression, decision trees, neural networks, etc.
Classification is a type of machine learning technique that predicts a discrete categorical value based on the input features. For example, predicting whether a vehicle is a car or a truck is a classification problem, because the output is a categorical value that can only take a few possible values. Classification models can use different algorithms to learn the relationship between the input features and the output value, such as logistic regression, k-nearest neighbors, support vector machines, etc.
Clustering is a type of machine learning technique that groups similar data points together based on their features. For example, clustering vehicles based on their size, color, and shape is a clustering problem, because the output is a set of clusters that contain similar vehicles. Clustering models can use different algorithms to learn the relationship between the data points and the clusters, such as k-means, hierarchical clustering, density-based clustering, etc.
Reference
Microsoft Learn > .NET > ML.NET guide > Resources > Machine learning tasks in ML.NET
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