Logistic Classification Model

A Logistic Classification Model is a Binary Classification Model based on the Logistic Function.

• AKA: Logistic Regression Model, LR Model.
• Context:
  • It can be trained by a Logistic Regression Algorithm.
• See: CRF-based Model, Naive-Bayes Model, Linear Classifier.

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References

2009

• (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/Logistic_regression
  • 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 is a generalized linear model used for binomial regression. 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.

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