Model validation involves splitting the dataset into training and validation sets or employing cross-validation techniques to assess performance. Metrics such as accuracy, precision, recall, or mean squared error are used to evaluate model performance, with additional testing on unseen data to ensure generalizability. Monitoring bias-variance tradeoff, assessing robustness under different conditions, and iterative refinement are crucial steps. While XAI techniques can enhance interpretability, they are not essential for basic model validation, which focuses on assessing predictive performance and generalization.

Machine Learning (ML) predictions. Methods and techniques have been developed at an incredible pace in the last 5 years, so much that now there is an entire field about it: XAI (eXplainable Artificial Intelligence).