The Information Theoretically Efficient Model (ITEM): A model for computerized analysis of large datasets
This addresses the need for efficient and interpretable models in data analysis, but appears incremental as it builds on existing logistic regression methods.
The paper tackles the problem of generating information-theoretically efficient multinomial logistic regression models for large datasets where the logit transform may not be linear, resulting in models that are tractable to compute on modern computers and resistant to overfitting.
This document discusses the Information Theoretically Efficient Model (ITEM), a computerized system to generate an information theoretically efficient multinomial logistic regression from a general dataset. More specifically, this model is designed to succeed even where the logit transform of the dependent variable is not necessarily linear in the independent variables. This research shows that for large datasets, the resulting models can be produced on modern computers in a tractable amount of time. These models are also resistant to overfitting, and as such they tend to produce interpretable models with only a limited number of features, all of which are designed to be well behaved.