Enhanced Logistic Regression Models Using Tree-Based Methods and Hybrid Modeling Techniques
Learn how you can enhance Logistic Regression Models to provide more insight into underlying relationships:
- Further elucidate relationships that are not as evident with standard logistic regression alone.
- Missing Data: When using Logistic Regression alone, you probably exclude records with missing data, or you exclude variables where data are missing even if these variables are highly predictive.
- Outliers: Deal better with outliers, catagorica; variables, departure from parametric assumptions, and multi-class response.
- Define a prediction rule with fewer predictor variables leading to easier interpretation.
- Uncover contingent effects that will be missed by Logistic Regression Modeling alone.