Understanding Deep Learning Using Explainable Machine Learning with LIME and H2O AutoML

Deep learning, a powerful artificial intelligence technique, has revolutionized fields like computer vision, natural language processing, and speech recognition. Despite its successes, the lack of interpretability and transparency in deep learning models hinders their understanding. To overcome this challenge, explainable machine learning (XAI) methods have been developed to shed light on the decision-making process of deep learning models. By utilizing techniques such as LIME and H2O AutoML, we can delve into the inner workings of deep learning models and enhance their interpretability. Our research involves applying these approaches to diverse real-world datasets and deep learning architectures, evaluating their performance, and generating meaningful explanations for the models’ predictions. This analysis aims to bridge the gap between the exceptional performance of deep learning and the crucial need for models that are interpretable and transparent. Through our work, we seek to advance the understanding of deep learning, ensuring that its capabilities are effectively harnessed while maintaining transparency and providing valuable insights into the decision-making processes of these models.