A Domain-Region Based Evaluation of ML Performance Robustness to Covariate Shift
This work addresses the problem of model reliability under distribution shifts for practitioners deploying ML systems, but it is incremental as it focuses on evaluation rather than proposing new solutions.
The paper experimentally evaluated the robustness of conventional machine learning models to covariate shift, finding that Random Forests was most robust in 2D cases with accuracy degradation as low as 0.1-2.08%, while higher-dimensional experiments showed degradation rates often exceeding 25% due to classification function complexity.
Most machine learning methods assume that the input data distribution is the same in the training and testing phases. However, in practice, this stationarity is usually not met and the distribution of inputs differs, leading to unexpected performance of the learned model in deployment. The issue in which the training and test data inputs follow different probability distributions while the input-output relationship remains unchanged is referred to as covariate shift. In this paper, the performance of conventional machine learning models was experimentally evaluated in the presence of covariate shift. Furthermore, a region-based evaluation was performed by decomposing the domain of probability density function of the input data to assess the classifier's performance per domain region. Distributional changes were simulated in a two-dimensional classification problem. Subsequently, a higher four-dimensional experiments were conducted. Based on the experimental analysis, the Random Forests algorithm is the most robust classifier in the two-dimensional case, showing the lowest degradation rate for accuracy and F1-score metrics, with a range between 0.1% and 2.08%. Moreover, the results reveal that in higher-dimensional experiments, the performance of the models is predominantly influenced by the complexity of the classification function, leading to degradation rates exceeding 25% in most cases. It is also concluded that the models exhibit high bias towards the region with high density in the input space domain of the training samples.