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Logistic regression is a kind of statistical analysis that is used to predict the outcome of a dependent variable based on prior observations. For example, an algorithm could determine the winner of a presidential election based on past election results and economic data. Logistic regression algorithms are popular in machine learning.
Logistic regression is a technique in statistical analysis that attempts to predict a data value based on prior observations. A logistic regression algorithm looks at the relationship between a dependent variable and one or more dependent variables.
Logistic regression has a number of applications in machine learning. A logistic regression algorithm might attempt to predict which candidate would win in an election by averaging all the polling results. A more sophisticated algorithm might also incorporate economic data and past elections in its model. Another algorithm might try to identify which users of a website would click on certain ads. It is also commonly used in database preparation to classify data for extract, transform and load (ETL) operations.