logistic regression, or logit regression, is a type of probabilistic statistical classification model. It is also used to predict a binary response from a binary predictor, used for predicting the outcome of a categorical dependent variable (i.e., a class label) based on one or more predictor variables (features).
The following matlab project contains the source code and matlab examples used for logistic regression.
The source code and files included in this project are listed in the project files section, please make sure whether the listed source code meet your needs there.
Project Files:
Four parameters logistic regression there and back again in matlab
Distributed logistic regression using expectation propagation. in matlab
Three parameters logistic regression there and back again in matlab
Five parameters logistic regression there and back again in matlab
This script shows how to perform an univariate logistic regression in matlab.
Logistic regression with regularization used to classify hand written digits in matlab
Logistic regression with regularization used to classify hand written digits in matlab
New regression capabilities in r2012a in matlab
Glmlab is a set of m-files for using matlab for analysing generalised linear models.
Fitting with matlab statistics, optimization, and curve fitting
Boosted generalized additive models (bgam) package in matlab
Hot to run weka classifiers within matlab
Accelerated failure time (aft) models in matlab
Online batch generalized linear models under square loss in matlab
Fit glm with quadratic penalty in matlab
Restricted cubic spline in matlab
Confusion matrix 3d with overall pcc and group statistics in matlab