A Paradigm for Potential Model Performance Improvement in Classification and Regression Problems. A Proof of Concept
This addresses a general problem in machine learning for practitioners, but it appears incremental as it builds on existing methods without a clear breakthrough.
The paper tackles the problem of improving model prediction performance in classification and regression by generating auxiliary models to create additional informative columns in datasets, with a proof of concept provided but no concrete numbers reported.
A methodology that seeks to enhance model prediction performance is presented. The method involves generating multiple auxiliary models that capture relationships between attributes as a function of each other. Such information serves to generate additional informative columns in the dataset that can potentially enhance target prediction. A proof of case and related code is provided.