Category: Logistic regression

Logistic regression
In statistics, the logistic model (or logit model) is a statistical model that models the probability of an event taking place by having the log-odds for the event be a linear combination of one or mo
Logit analysis in marketing
Logit analysis is a statistical technique used by marketers to assess the scope of customer acceptance of a product, particularly a new product. It attempts to determine the intensity or magnitude of
Standard logistic function
No description available.
Local case-control sampling
In machine learning, local case-control sampling is an algorithm used to reduce the complexity of training a logistic regression classifier. The algorithm reduces the training complexity by selecting
Elastic net regularization
In statistics and, in particular, in the fitting of linear or logistic regression models, the elastic net is a regularized regression method that linearly combines the L1 and L2 penalties of the lasso
Logistic function
A logistic function or logistic curve is a common S-shaped curve (sigmoid curve) with equation where , the value of the sigmoid's midpoint;, the supremum of the values of the function;, the logistic g
Separation (statistics)
In statistics, separation is a phenomenon associated with models for dichotomous or categorical outcomes, including logistic and probit regression. Separation occurs if the predictor (or a linear comb
Conditional logistic regression
Conditional logistic regression is an extension of logistic regression that allows one to take into account stratification and matching. Its main field of application is observational studies and in p
Hosmer–Lemeshow test
The Hosmer–Lemeshow test is a statistical test for goodness of fit for logistic regression models. It is used frequently in risk prediction models. The test assesses whether or not the observed event
Softmax function
The softmax function, also known as softargmax or normalized exponential function, converts a vector of K real numbers into a probability distribution of K possible outcomes. It is a generalization of
Ordered logit
In statistics, the ordered logit model (also ordered logistic regression or proportional odds model) is an ordinal regression model—that is, a regression model for ordinal dependent variables—first co
Variable rules analysis
In linguistics, variable rules analysis is a set of statistical analysis methods commonly used in sociolinguistics and historical linguistics to describe patterns of variation between alternative form
Multinomial logistic regression
In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes. That is, it i
Bradley–Terry model
The Bradley–Terry model is a probability model that can predict the outcome of a paired comparison. Given a pair of individuals i and j drawn from some population, it estimates the probability that th