Generalized linear model & Logistic regression

• Generalized linear model - Wikipedia, the free encyclopedia

  tags: algorithm

  • In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary least squares regression. It relates the random distribution of the measured variable of the experiment (the distribution function) to the systematic (non-random) portion of the experiment (the linear predictor) through a function called the link function.

• Logistic regression - Wikipedia, the free encyclopedia

  In statistics, logistic regression is a model used for prediction of the probability of occurrence of an event by fitting data to a logistic curve. It makes use of several predictor variables that may be either numerical or categorical. For example, the probability that a person has a heart attack within a specified time period might be predicted from knowledge of the person's age, sex and body mass index. Logistic regression is used extensively in the medical and social sciences as well as marketing applications such as prediction of a customer's propensity to purchase a product or cease a subscription. Other names for logistic regression used in various other application areas include logistic model, logit model, and maximum-entropy classifier. Logistic regression is one of a class of models known as generalized linear models.

  tags: logistic, regression

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