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  2. 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 ...

  3. Logistic model tree - Wikipedia

    en.wikipedia.org/wiki/Logistic_model_tree

    Logistic model trees are based on the earlier idea of a model tree: a decision tree that has linear regression models at its leaves to provide a piecewise linear regression model (where ordinary decision trees with constants at their leaves would produce a piecewise constant model). [1] In the logistic variant, the LogitBoost algorithm is used ...

  4. Multinomial logistic regression - Wikipedia

    en.wikipedia.org/.../Multinomial_logistic_regression

    Multinomial logistic regression is known by a variety of other names, including polytomous LR, [2] [3] multiclass LR, softmax regression, multinomial logit (mlogit), the maximum entropy (MaxEnt) classifier, and the conditional maximum entropy model.

  5. Logistic function - Wikipedia

    en.wikipedia.org/wiki/Logistic_function

    Logistic regression and other log-linear models are also commonly used in machine learning. A generalisation of the logistic function to multiple inputs is the softmax activation function, used in multinomial logistic regression. Another application of the logistic function is in the Rasch model, used in item response theory.

  6. Hosmer–Lemeshow test - Wikipedia

    en.wikipedia.org/wiki/Hosmer–Lemeshow_test

    The Hosmer–Lemeshow 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 assesses whether or not the observed event rates match expected event rates in subgroups of the model population.

  7. Elastic net regularization - Wikipedia

    en.wikipedia.org/wiki/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 L 1 and L 2 penalties of the lasso and ridge methods. Nevertheless, elastic net regularization is typically more accurate than both methods with regard to reconstruction. [1]

  8. 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.

  9. Conditional logistic regression - Wikipedia

    en.wikipedia.org/.../Conditional_logistic_regression

    Logistic regression as described above works satisfactorily when the number of strata is small relative to the amount of data. If we hold the number of strata fixed and increase the amount of data, estimates of the model parameters ( α i {\displaystyle \alpha _{i}} for each stratum and the vector β {\displaystyle {\boldsymbol {\beta ...