While, on the one hand, such approaches have shown remarkable success , it is important to acknowledge their limitations and potential downsides. One significant challenge is the difficulty in assessing the quality of performance outside the distribution of training data. As it happens, ML models can learn patterns that are too specific to the training data and fail to extrapolate to unseen data (overfitting). Such approaches also heavily rely on the size of the training data and scarcity can lead to models with limited capabilities or inaccurate predictions. Experimental datasets of specific chemical properties, such as thermoelectric properties , are very unbalanced and rare throughout the literature. This is a consequence of the popular material repositories predominantly relying on DFT calculations , which tend to provide a constrained selection of chemical attributes. This hampers the ability to effectively target specific material classes. Different approaches have been adopted to mitigate the natural presence of bias in materials data. LOCO-CV has been proposed as a modification of the standard KFold evaluation strategy to measure the extrapolation error of machine learning models on unseen chemical clusters. Furthermore, an entropy-based metric has been recently proposed to mitigate the imbalance of a crystal structures dataset by improving the diversity of underrepresented crystal systems On a general level, three main strands are usually considered to improve the predictive accuracy of ML models: