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.

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:

• Perform Quantile Analysis and Regression of your model scores
• Build and analyze ROC Curves
• Determine Optimal Cutoff Points for your model scores
• Perform detailed analyses of Gains and Lift Charts
• Create accurate Logistic Regression Models
• Assign probabilities to your model scores
• Build and analyze Logistic Fit Charts
• Infer and analyze Logistic and ROC Confusion Matrices
• Test the predictive accuracy of your models on unseen data
• Rank new cases easily using the Logistic Regression Scoring Engine
• Explore vast solution spaces with a wide set of fitness functions
• Evolve models using different class encodings
• Convert Classification runs to the Logistic Regression Framework
• Convert Logistic Regression runs to the Classification Framework