# Logistic Function Family

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A Logistic Function Family is a function family that is restricted to logistic functions (of the form of the form [math]\displaystyle{ f(t,A,B,C) \equiv (C + Ae^{-Bt})^{-1} }[/math], where [math]\displaystyle{ A, B, C }[/math] are model parameters.)

**AKA:**Maximum Entropy Model.**Context:**- It can be an input to a Logistic Regression Task (which results in a fitted logistic function).
- It is in a Generative-Discriminative Relation with a Naive Bayes Model.
- …

**Counter-Example(s):****See:**Logit Function.

## References

### 2007

- http://www.nature.com/nrg/journal/v4/n9/glossary/nrg1155_glossary.html
- LOGISTIC REGRESSION MODEL: A statistical model for the dependency of a binomial (two-class) phenotype on a number of risk factors. The probability, p, for one of the two phenotype states is expressed in the form of its logit, log(p/(1 – p)), which is assumed to be predicted by the linear combination (weighted sum) of the risk factors.