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Logistic Regression Framework

Introduction

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Logistic Regression Analytics Platform

The goal in Logistic Regression is to assign probabilities to model scores, creating a reliable ranking system that can be used straightaway to evaluate the risk involved in financial and insurance applications, to rank potential respondents in a marketing campaign, or to evaluate the risk of contracting a disease.

Quantile Analysis - Logistic Regression Window

The Logistic Regression Framework of GeneXproTools builds on the models it generates with its evolutionary algorithms and then applies the canonical logistic regression technique to estimate probabilities for each model score. And once you know the probability of an event, you can also make categorical predictions and consequently infer crisp confusion matrices.

Thus, with its new Logistic Regression Framework, GeneXproTools offers a highly robust hybrid system in which sophisticated multivariate nonlinear models are easily created by evolution and then empowered by traditional statistical modeling techniques.


With the Logistic Regression Framework of GeneXproTools you can:



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Logistic Regression Framework

   
   

"I have been using GeneXproTools against a variety of drug research related problems and have found the GUI to be readily-usable and well-attuned to the stages of predictive modelling (...much easier than the command line software I'm used to using!!)."

Steven J Barrett, Ph.D
Principal Scientist
New Applications Team
Data Exploration Sciences
GlaxoSmithKline

   

"GeneXproTools is being used to look at problems involving parasite populations, where the data is highly skewed. The results using GeneXproTools are considerably better than those obtained using conventional statistics."

Prof John Barrett
Head of the Parasitology Group
University of Wales, UK

   
 

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