Discover how regression, a type of supervised machine learning, can be used to predict the number of vehicles that will cross a bridge on a given day. Learn about regression models for traffic forecasting and how they differ from classification and clustering techniques.
Table of Contents
Question
Predicting how many vehicles will travel across a bridge on a given day is an example of what type of machine learning? Select the correct option.
A. classification.
B. regression.
C. clustering.
Answer
B. regression.
Explanation
Predicting how many vehicles will travel across a bridge on a given day is an example of regression, which is a type of supervised machine learning.
Regression is used when the output variable we want to predict is a continuous numerical value, such as the number of vehicles crossing a bridge. Regression models learn from historical data with known input features (like day of week, season, weather conditions) and their corresponding output values (actual traffic counts). The model finds patterns and relationships in this training data that allow it to make predictions on new, unseen data points.
Some key characteristics of regression problems:
- The target variable is continuous and quantitative (e.g. counts, amounts, prices)
- The goal is to predict a specific numerical value, not just a category or class
- Common algorithms include linear regression, polynomial regression, decision trees, neural networks
In contrast:
A) Classification is used when predicting a categorical target variable, like determining if an email is spam or not spam. It doesn’t output a numerical quantity.
C) Clustering is an unsupervised learning technique that groups similar data points together based on their features, without predicting any specific output value.
So in summary, using historical data to build a model that can predict the specific number of vehicles that will cross a bridge on a future date is a canonical example of a regression machine learning task. The continuous numeric quantity being predicted is the key factor categorizing this as regression rather than classification or clustering.
Predicting how many vehicles will travel across a bridge on a given day involves forecasting a numerical value, which is an example of regression in machine learning.
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