Mispredictions by machine learning components can have severe consequences, especially in safety-critical and mission-critical software systems. Therefore, understanding and debugging these mispredictions is a crucial part of the development process for systems that use machine learning components. Previous research has successfully applied methods that identify when a model’s predictions may be unreliable by generating a rule set that links feature values to prediction errors. However, current state-of-the-art rule set approaches require significant computational resources, particularly for large data sets.

To address these high computational demands, we propose a strategy to identify and focus only on the most influential features that lead to mispredictions. Additionally, to improve the accuracy of mispredictions diagnosis, we replace traditional rule-based approaches with decision tree learning. We evaluate our tool \MMDPP{} across 11 diverse real-world data sets. The results show that focusing on influential features with decision trees improves the accuracy of misprediction explanations, while significantly reducing computational demands in all scenarios. Thus, \MMDPP{} produces better results much faster, making it more efficient and effective for generating misprediction explanations.