independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (the coefficients...

etc.). Multinomial logistic regression is known by a variety of other names, including polytomous LR, multiclass LR, softmax regression, multinomial logit...

Conditional logistic regression is an extension of logistic regression that allows one to account for stratification and matching. Its main field of application...

distribution plays the same role in logistic regression as the normal distribution does in probit regression. Indeed, the logistic and normal distributions have...

A logistic function or logistic curve is a common S-shaped curve (sigmoid curve) with the equation f ( x ) = L 1 + e − k ( x − x 0 ) {\displaystyle f(x)={\frac...

the cross-entropy loss for logistic regression is the same as the gradient of the squared-error loss for linear regression. That is, define X T = ( 1...

logit model (also ordered logistic regression or proportional odds model) is an ordinal regression model—that is, a regression model for ordinal dependent...

In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is...

common binary regression models are the logit model (logistic regression) and the probit model (probit regression). Binary regression is principally...

classifiers form a generative-discriminative pair with multinomial logistic regression classifiers: each naive Bayes classifier can be considered a way...

linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where...

categorical dependent variable (i.e. the class label). Logistic regression and probit regression are more similar to LDA than ANOVA is, as they also explain...

In statistics, ordinal regression, also called ordinal classification, is a type of regression analysis used for predicting an ordinal variable, i.e....

Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes...

predictive performance than other linear models, such as logistic regression and linear regression.[citation needed] Classifying data is a common task in...

6.332 on 2 and 7 DF, p-value: 0.02692 In statistics, logistic regression is a type of regression analysis used for predicting the outcome of a categorical...

In statistics, stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic...

Many algorithms, including support-vector machines, linear regression, logistic regression, neural networks, and nearest neighbor methods, require that...

Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated...

"multilevel regression" and "poststratification" ideas of MRP can be generalized. Multilevel regression can be replaced by nonparametric regression or regularized...

used, since this is more familiar in everyday life". The logit in logistic regression is a special case of a link function in a generalized linear model:...

response model. As such it treats the same set of problems as does logistic regression using similar techniques. When viewed in the generalized linear model...

particular, in the fitting of linear or logistic regression models, the elastic net is a regularized regression method that linearly combines the L1 and...

Look up regression, regressions, or régression in Wiktionary, the free dictionary. Regression or regressions may refer to: Marine regression, coastal advance...

distribution (the Bernoulli distribution) and separate regression models (logistic regression, probit regression, etc.). As a result, the term "categorical variable"...

Logistic equation can refer to: Logistic map, a nonlinear recurrence relation that plays a prominent role in chaos theory Logistic regression, a regression...

Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. Its...

model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that sense it is...

test is a statistical test for goodness of fit and calibration for logistic regression models. It is used frequently in risk prediction models. The test...

regression does. Linear regression assumes homoscedasticity, that the error variance is the same for all values of the criterion. Logistic regression...