Logistic Regression

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What Does Logistic Regression Mean?

Logistic regression is a supervised learning algorithm used in machine learning to predict the probability of a binary outcome. A binary outcome is limited to one of two possible outcomes. Examples include yes/no, 0/1 and true/false.

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Logical regression is used in predictive modeling to analyze large datasets in which one or more independent variables can determine an outcome. The outcome is expressed as a dichotomous variable that has one of two possible outcomes.

Essentially, logistic regression works by estimating the mathematical probability that an instance belongs to a specified class — or not.

Techopedia Explains Logistic Regression

Logistic regression uses something called the Sigmoid function to map predicted predictions and their probabilities. On a graph, if the estimated probability is greater than a pre-defined acceptance threshold, then the model will predict that the instance belongs to that class. If the estimated probability is less than the pre-defined threshold on the graph, then the model will predict the instance does not belong to the class.

In statistics, there are three basic types of logistic regression:

Binary logistic regression useful for predicting the relationship between a binary dependent variable (Y) and an independent variable (X).

Multinomial logistic regression — useful for making predictions when the dependent variable has two or more discrete outcomes and the order of the outcomes doesn't matter.

Ordinal logistic regression — useful for making predictions when the dependent variable has more than two discrete outcomes and the order of the outcomes has some significance.

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Margaret Rouse
Technology Expert
Margaret Rouse
Technology Expert

Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.