Probit Function Training Algorithm
A Probit Function Training Algorithm is a discriminative parametric regression binary classification algorithm that can solve a probit regression task (by producing a fitted probit classification function).
- AKA: Probit Regression.
- See: Logistic Regression Algorithm.
References
2013
- http://en.wikipedia.org/wiki/Probit_model
- In statistics, a probit model is a type of regression where the dependent variable can only take two values, for example married or not married. The name is from probability + unit.[1] The purpose of the model is to estimate the probability that an observation with particular characteristics will fall into a specific one of the categories; moreover, if estimated probabilities greater than 1/2 are treated as classifying an observation into a predicted category, the probit model is a type of binary classification model.
A probit model is a popular specification for an ordinal[2] or a binary response model. As such it treats the same set of problems as does logistic regression using similar techniques. The probit model, which employs a probit link function, is most often estimated using the standard maximum likelihood procedure, such an estimation being called a probit regression.
Probit models were introduced by Chester Bliss in 1934; a fast method for computing maximum likelihood estimates for them was proposed by Ronald Fisher as an appendix to Bliss' work in 1935.
- In statistics, a probit model is a type of regression where the dependent variable can only take two values, for example married or not married. The name is from probability + unit.[1] The purpose of the model is to estimate the probability that an observation with particular characteristics will fall into a specific one of the categories; moreover, if estimated probabilities greater than 1/2 are treated as classifying an observation into a predicted category, the probit model is a type of binary classification model.
- ↑ Oxford English Dictionary, 3rd ed. s.v. probit (article dated June 2007): C. I. Bliss in Science 12 Jan. 1934, 38/1, "These arbitrary probability units have been called ‘probits’."
- ↑ Ordinal probit regression model UCLA Academic Technology Services http://www.ats.ucla.edu/stat/stata/dae/ologit.htm