Exam Professional Machine Learning Engineer topic 1 question 206 discussion

The question is about image/pixels. So the integrated Gradients is better. Shapley is for input features.

Use Integrated Gradients to explain the model output

https://cloud.google.com/vertex-ai/docs/explainable-ai/overview

Given the scenario of using high definition images as inputs for predictive maintenance on bridges, and the need to explain the model output to stakeholders, the most appropriate choice would be: C. Use TensorFlow to create a deep learning-based model, and use Integrated Gradients to explain the model output. Integrated Gradients is a method used to explain the predictions of deep learning models by attributing the contribution of each pixel in the input image to the final prediction. This would provide insights into which parts of the bridge images are most influential in the model's decision-making process, helping stakeholders understand why a particular prediction was made and allowing them to take appropriate action.

https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/overview#compare-methods

Handling image input: Deep learning models excel in processing complex visual data like high-definition images, making them ideal for extracting relevant features from bridge images for defect detection. Explainability with Integrated Gradients: Integrated Gradients is a powerful technique specifically designed to explain the predictions of deep learning models. It attributes model output to specific input features, providing insights into how the model makes decisions. Visualization: Integrated Gradients can generate visual explanations, such as heatmaps, that highlight image regions most influential to predictions, aiding in understanding and trust for stakeholders.