What distinguishes linear regression from logistic regression?
Understand the Problem
The question is asking for the distinctions between linear regression and logistic regression, specifically which statement accurately describes these differences. It presents multiple-choice options that indicate varying characteristics of both types of regression.
Answer
Linear regression predicts continuous outcomes, logistic regression predicts categorical outcomes.
Linear regression predicts continuous outcomes and assumes a normal distribution of the dependent variable, whereas logistic regression predicts categorical outcomes and follows a binomial distribution.
Answer for screen readers
Linear regression predicts continuous outcomes and assumes a normal distribution of the dependent variable, whereas logistic regression predicts categorical outcomes and follows a binomial distribution.
More Information
Linear regression is commonly used for tasks like predicting a person's salary based on years of experience, while logistic regression might be used to predict whether an email is spam or not.
Tips
A common mistake is to use logistic regression for continuous outcomes, which should be modeled with linear regression. Similarly, using linear regression for binary classifications can lead to inappropriate predictions.
Sources
- Understanding The Difference Between Linear vs Logistic Regression - simplilearn.com
- Linear vs. Logistic Regression - Spiceworks - spiceworks.com
- Difference between linear regression and logistic regression - stats.stackexchange.com
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