enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Multinomial logistic regression - Wikipedia

    en.wikipedia.org/wiki/Multinomial_logistic...

    The difference between the multinomial logit model and numerous other methods, models, algorithms, etc. with the same basic setup (the perceptron algorithm, support vector machines, linear discriminant analysis, etc.) is the procedure for determining (training) the optimal weights/coefficients and the way that the score is interpreted.

  3. Discrete choice - Wikipedia

    en.wikipedia.org/wiki/Discrete_choice

    The "mixed exploded logit" model is obtained by probability of the ranking, given above, for L ni in the mixed logit model (model I). This model is also known in econometrics as the rank ordered logit model and it was introduced in that field by Beggs, Cardell and Hausman in 1981.

  4. Choice modelling - Wikipedia

    en.wikipedia.org/wiki/Choice_modelling

    These often begin with the conditional logit model - traditionally, although slightly misleadingly, referred to as the multinomial logistic (MNL) regression model by choice modellers. The MNL model converts the observed choice frequencies (being estimated probabilities, on a ratio scale) into utility estimates (on an interval scale) via the ...

  5. Logistic regression - Wikipedia

    en.wikipedia.org/wiki/Logistic_regression

    In statistics, the logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more independent variables. In regression analysis , logistic regression [ 1 ] (or logit regression ) estimates the parameters of a logistic model (the coefficients in the linear or non linear ...

  6. Multinomial probit - Wikipedia

    en.wikipedia.org/wiki/Multinomial_probit

    In statistics and econometrics, the multinomial probit model is a generalization of the probit model used when there are several possible categories that the dependent variable can fall into. As such, it is an alternative to the multinomial logit model as one method of multiclass classification .

  7. Logit-normal distribution - Wikipedia

    en.wikipedia.org/wiki/Logit-normal_distribution

    In probability theory, a logit-normal distribution is a probability distribution of a random variable whose logit has a normal distribution.If Y is a random variable with a normal distribution, and t is the standard logistic function, then X = t(Y) has a logit-normal distribution; likewise, if X is logit-normally distributed, then Y = logit(X)= log (X/(1-X)) is normally distributed.

  8. Multinomial logit model - Wikipedia

    en.wikipedia.org/?title=Multinomial_logit_model&...

    Retrieved from "https://en.wikipedia.org/w/index.php?title=Multinomial_logit_model&oldid=522106095"https://en.wikipedia.org/w/index.php?title=Multinomial_logit_model

  9. Softmax function - Wikipedia

    en.wikipedia.org/wiki/Softmax_function

    It is a generalization of the logistic function to multiple dimensions, and is used in multinomial logistic regression. The softmax function is often used as the last activation function of a neural network to normalize the output of a network to a probability distribution over predicted output classes.