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logistic regression
This term refers to a specific mathematical model used in statistics and machine learning. It is distinct from linear regression because it predicts a categorical outcome—typically a binary yes/no or true/false result—rather than a continuous numerical value.
In professional and academic contexts, it is used across diverse fields such as epidemiology, finance, and sociology to identify the strength of relationships between a dependent binary variable and one or more predictor variables. It is almost exclusively used as a technical term in formal research and data analysis.
Meanings
A statistical method used to predict the probability of a binary outcome based on one or more independent variables.
The researcher used logistic regression to determine the likelihood of a patient developing a disease based on their age and weight.
Examples
We used logistic regression to determine the probability of customer churn.
I think logistic regression is the best approach for this binary classification problem.
The researchers applied logistic regression to analyze the risk factors for heart disease.
Does this dataset meet the assumptions required for logistic regression?
A simple logistic regression model can predict whether a loan application will be approved.
The study employs logistic regression to estimate the likelihood of success based on prior experience.
The study employs logistic regression to estimate the likelihood of success based on prior experience.
Wait, why are you using linear regression when you should be using logistic regression?
Logistic regression provides a clear coefficient for each predictor variable.
Logistic regression provides a clear coefficient for each predictor variable.