independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (the coefficients...
127 KB (20,600 words) - 23:20, 20 May 2024
etc.). Multinomial logistic regression is known by a variety of other names, including polytomous LR, multiclass LR, softmax regression, multinomial logit...
30 KB (5,206 words) - 15:05, 19 May 2024
Conditional logistic regression is an extension of logistic regression that allows one to account for stratification and matching. Its main field of application...
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distribution plays the same role in logistic regression as the normal distribution does in probit regression. Indeed, the logistic and normal distributions have...
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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...
45 KB (6,102 words) - 02:34, 30 April 2024
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...
18 KB (3,122 words) - 20:27, 12 May 2024
Ordered logit (redirect from Ordered logistic regression)
logit model (also ordered logistic regression or proportional odds model) is an ordinal regression model—that is, a regression model for ordinal dependent...
8 KB (1,122 words) - 06:52, 20 May 2024
Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes...
18 KB (2,744 words) - 15:07, 29 April 2024
In statistics, ordinal regression, also called ordinal classification, is a type of regression analysis used for predicting an ordinal variable, i.e....
10 KB (1,301 words) - 12:19, 12 February 2024
In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is...
14 KB (2,055 words) - 17:53, 26 January 2024
common binary regression models are the logit model (logistic regression) and the probit model (probit regression). Binary regression is principally...
4 KB (581 words) - 20:28, 27 March 2022
classifiers form a generative-discriminative pair with multinomial logistic regression classifiers: each naive Bayes classifier can be considered a way...
35 KB (5,488 words) - 23:21, 15 March 2024
linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where...
70 KB (9,686 words) - 05:47, 13 May 2024
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...
46 KB (5,950 words) - 07:10, 23 May 2024
Support vector machine (redirect from Support vector regression)
predictive performance than other linear models, such as logistic regression and linear regression.[citation needed] Classifying data is a common task in...
63 KB (8,914 words) - 01:06, 24 May 2024
Omnibus test (section In logistic regression)
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...
44 KB (6,180 words) - 08:34, 5 February 2024
Many algorithms, including support-vector machines, linear regression, logistic regression, neural networks, and nearest neighbor methods, require that...
22 KB (3,011 words) - 10:15, 25 April 2024
In statistics, stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic...
11 KB (1,483 words) - 00:01, 21 April 2024
Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated...
30 KB (3,902 words) - 03:51, 25 March 2024
"multilevel regression" and "poststratification" ideas of MRP can be generalized. Multilevel regression can be replaced by nonparametric regression or regularized...
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Logit (redirect from Logistic transform)
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:...
11 KB (1,423 words) - 20:26, 6 April 2024
Probit model (redirect from Probit regression)
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...
20 KB (3,251 words) - 00:39, 18 April 2024
Elastic net regularization (redirect from Elastic net regression)
particular, in the fitting of linear or logistic regression models, the elastic net is a regularized regression method that linearly combines the L1 and...
12 KB (1,391 words) - 07:34, 22 December 2023
Look up regression, regressions, or régression in Wiktionary, the free dictionary. Regression or regressions may refer to: Marine regression, coastal advance...
2 KB (235 words) - 14:22, 18 October 2023
Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. Its...
18 KB (2,557 words) - 14:15, 17 January 2024
distribution (the Bernoulli distribution) and separate regression models (logistic regression, probit regression, etc.). As a result, the term "categorical variable"...
22 KB (3,051 words) - 16:55, 25 March 2024
Logistic equation can refer to: Logistic map, a nonlinear recurrence relation that plays a prominent role in chaos theory Logistic regression, a regression...
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General linear model (redirect from Multivariate regression model)
model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that sense it is...
11 KB (1,192 words) - 07:42, 24 May 2024
Hosmer–Lemeshow test (category Logistic regression)
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...
19 KB (2,545 words) - 12:17, 16 May 2024
Pseudo-R-squared (category Regression diagnostics)
regression does. Linear regression assumes homoscedasticity, that the error variance is the same for all values of the criterion. Logistic regression...
7 KB (897 words) - 20:11, 18 April 2024