Evaluating and Improving Machine Learning Models for Regioselectivity Prediction in Electrophilic Aromatic Substitution Reactions

In recent years, machine learning models have become increasingly powerful and widely used in various applications. However, these models can sometimes perform poorly when faced with unseen data, which poses a significant challenge to their reliability and robustness. This problem is known as "out-of-distribution" (OOD) detection, and it has been the focus of much research in recent years.
In this article, we survey the state-of-the-art methods for OOD detection in machine learning models. We begin by explaining the concept of OOD detection and why it is important. We then discuss the different approaches to OOD detection, including feature-based methods, instance-based methods, and hybrid methods.
Feature-based methods rely on extracting features from the input data that can distinguish between in-distribution (ID) and OOD data. Instance-based methods, on the other hand, evaluate each individual instance rather than relying on features. Hybrid methods combine both feature-based and instance-based approaches.
We then examine the different evaluation metrics used to measure the performance of OOD detection methods, including accuracy, precision, recall, and F1-score. We also discuss the challenges associated with evaluating OOD detection methods, such as the lack of standardized datasets and the difficulty in obtaining accurate labels for OOD data.
Finally, we highlight some of the most promising OOD detection methods in recent years, including those based on adversarial training, generative models, and meta-learning. We also discuss some of the open challenges and future research directions in this field.
In summary, this article provides a comprehensive overview of the state-of-the-art methods for OOD detection in machine learning models. It covers the different approaches to OOD detection, evaluation metrics, and challenges associated with evaluating these methods. The article also highlights some of the most promising methods and future research directions in this field.