In this tutorial, we show how to implement a multinomial logistic regression with TANAGRA.
Logistic regression is a technique for making predictions when the dependent variable is a dichotomy, and the independent variables are continuous and/or discrete. The technique can be modified to handle dependent variable with several (K > 2) levels.
When the responses categories are unordered, we have the multinomial logistic regression. Roughly speaking, we compute the logit function for each (K-1) categories related to a reference group.
Keywords: multinomial logistic regression
Components: Supervised Learning, Multinomial Logistic Regression
Tutorial: en_Tanagra_Multinomial_Logistic_Regression.pdf
Dataset: brand_multinomial_logit_dataset.xls
References:
A. Slavkovic, « Multinomial Logistic Regression Models – Baseline-Category Logit Model », in « STAT 504 – Analysis of Discrete Data », Pensylvania State University, 2007.
