  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.
