A comparative study of six model complexity metrics to search for parsimonious models with GAparsimony R Package

Abstract

Nowadays, interest is growing in automating KDD processes. Thanks to the increasing power and decreasing costs of computation devices, the search for the best features and model parameters can be conducted with different meta-heuristics. Thus, researchers can focus on other important tasks like data wrangling or feature engineering. This article details a comparative study of a GAparsimony R Package with six model complexity metrics. The objective was to identify an adequate model complexity measure for searching for accurate parsimonious solutions by combining feature selection, hyperparameter optimization, and parsimonious evaluation. This study also includes a regression code example to address some recommended precautions and considerations to find robust parsimonious models. This code can be easily adapted to other problems, databases, or algorithms.

Tasks

Reproductions