Summary
In this chapter, we gained a broad view of the prediction explanations landscape and dived into the integrated gradients technique, applied it practically to a use case, and even attempted to explain the integrated gradients results manually and automatically through LLMs. We also discussed common pitfalls in prediction explanations and provided strategies to avoid them, ensuring the effectiveness of these explanations in understanding and improving AI models.
Integrated gradients is a useful technique and tool to provide a form of saliency-based explanation of the predictions that your neural network makes. The process of understanding a model through prediction explanations provides many benefits that can help fulfill the criteria required to have a successful machine learning project and initiative. Even when everything is going well and the machine learning use case is not critical, uncovering the model’s behavior that you will potentially deploy through any prediction...
