Novel binning-based methods for model fitting and data splitting improved machine learning imbalanced data

Abstract Machine Learning (ML) models may perform inconsistently on individual classes on nominal outputs or ranges on continuous outputs, collectively referred to here as bins. Models should be assessed through metrics that consider each bin individually, called bin metrics. Inconsistent model performance is often due to model fitting with imbalanced data. Towards improving modelling of imbalanced data, novel model fitting methods are proposed including using bin metrics as loss functions and the use of Epoch sampling. Imbalanced data also poses a challenge for appropriate data splitting. Akin split is a novel method proposed that objectively yields the most appropriate data split(s). Existing and novel model fitting methods were used to fit models, and the models were assessed by a bin metric in in two case studies. The first case study used synthetically generated datasets with different levels of noise and imbalance. On datasets with noise and greater levels of imbalance, Epoch sampling significantly improved the model performance by up to 23.6% while significantly using less resources (computation and time) by up to 57.7% compared to a standard model fitting method. The second case study used protein-genome interactions data that are often severely right-skewed. Akin split was used to split the data more appropriately than traditional methods. Model fitting methods were tried on two model configurations. The effects of the model fitting methods varied by the model configuration, but all models were significantly improved by up to 57.7% compared to the standard model fitting.