glmdisc

This module is dedicated to preprocessing tasks for logistic regression and post-learning graphical tools.

Glmdisc([algorithm, test, validation, …])	This class implements a supervised multivariate discretization method, factor levels grouping and interaction discovery for logistic regression.
Glmdisc._check_is_fitted()	Perform is_fitted validation for estimator.
Glmdisc.best_formula()	Returns the best quantization found by the MCMC and prints it.
Glmdisc.discrete_data()	Returns the best discrete data (train, validation or test) found by the MCMC.
Glmdisc.discretize(predictors_cont, …)	Discretizes new continuous and categorical features using a previously fitted glmdisc object.
Glmdisc.discretize_dummy(predictors_cont, …)	Discretizes new continuous and categorical features using a previously fitted glmdisc object as Dummy Variables usable with the best_reglog object.
Glmdisc.fit(predictors_cont, …[, iter])	Fits the Glmdisc object.
Glmdisc.plot([predictors_cont_number, …])	Plots the stepwise function associating the continuous features to their discretization, the groupings made and the interactions.
Glmdisc.predict(predictors_cont, predictors_qual)	Predicts the label values with new continuous and categorical features using a previously fitted glmdisc object.
Glmdisc.generate_data(n, d[, theta, plot])	Generates some toy continuous data that gets discretized, and a label is drawn from a logistic regression given the discretized features.
NotFittedError	Exception class to raise if estimator is used before fitting.

Classes

Glmdisc([algorithm, test, validation, …])

This class implements a supervised multivariate discretization method, factor levels grouping and interaction discovery for logistic regression.

Exceptions

NotFittedError

Exception class to raise if estimator is used before fitting.