What is the purpose of logistic regression? Logistic regression is one of various data modeling techniques used to forecast outcomes. Machine learning models use and train on a combination of input and output data and use new data to predict the output. Regression models essentially represent or encapsulate a mathematical equation that approximates the interactions between the different variables being modeled. "Predictive analytics tools can broadly be classified as traditional regression-based tools or machine learning-based tools," said Donncha Carroll, a partner in the revenue growth practice of Axiom Consulting Partners. Regression is a cornerstone of modern predictive analytics applications.
Subsequent researchers adopted the term to describe a process for representing the effect of independent variables on probability. In contrast, logistic (without the s) characterizes a mathematical technique for dividing phenomena into two categories.įrancis Galton coined the term regression in 1889 to characterize a biological phenomenon in which tall people's descendants regress toward the average heights of the population.
It is not linked to logistics, which evolved separately from a French word to describe a process for optimizing complex supply chain calculations. The etymology of logistic regression is a bit confusing. 7 top predictive analytics use cases: Enterprise examples.This article is part of What is predictive analytics? An enterprise guide Logistic regression can also play a role in data preparation activities by allowing data sets to be put into specifically predefined buckets during the extract, transform, load ( ETL) process in order to stage the information for analysis. As additional relevant data comes in, the algorithms get better at predicting classifications within data sets. It allows algorithms used in machine learning applications to classify incoming data based on historical data. Logistic regression has become an important tool in the discipline of machine learning. Based on historical data about earlier outcomes involving the same input criteria, it then scores new cases on their probability of falling into one of two outcome categories. In the case of college acceptance, the logistic function could consider factors such as the student's grade point average, SAT score and number of extracurricular activities. These binary outcomes allow straightforward decisions between two alternatives.Ī logistic regression model can take into consideration multiple input criteria. For example, a logistic regression could be used to predict whether a political candidate will win or lose an election or whether a high school student will be admitted or not to a particular college. Logistic regression is a statistical analysis method to predict a binary outcome, such as yes or no, based on prior observations of a data set.Ī logistic regression model predicts a dependent data variable by analyzing the relationship between one or more existing independent variables.