Automated Model Selection for Tabular Data
This addresses the difficulty of model selection for researchers and practitioners working with tabular datasets, but it is incremental as it builds on existing methods like R's mixed effect models.
The paper tackles the problem of automating model selection for tabular data with feature interactions, which is computationally challenging due to exponential possibilities, and presents a framework using Priority-based Random Grid Search and Greedy Search methods to efficiently capture predictive combinations while keeping costs low, with experiments on synthetic data showing effectiveness.
Structured data in the form of tabular datasets contain features that are distinct and discrete, with varying individual and relative importances to the target. Combinations of one or more features may be more predictive and meaningful than simple individual feature contributions. R's mixed effect linear models library allows users to provide such interactive feature combinations in the model design. However, given many features and possible interactions to select from, model selection becomes an exponentially difficult task. We aim to automate the model selection process for predictions on tabular datasets incorporating feature interactions while keeping computational costs small. The framework includes two distinct approaches for feature selection: a Priority-based Random Grid Search and a Greedy Search method. The Priority-based approach efficiently explores feature combinations using prior probabilities to guide the search. The Greedy method builds the solution iteratively by adding or removing features based on their impact. Experiments on synthetic demonstrate the ability to effectively capture predictive feature combinations.