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Answer: Use TensorFlow to create a deep learning-based model, and use Integrated Gradients to explain the model output.
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 is to 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.
Author: LeetQuiz Editorial Team
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You are building a predictive maintenance model to preemptively detect part defects in bridges, which is crucial for ensuring safety and longevity. You plan to use high definition images of the bridges as model inputs, utilizing advanced image processing and machine learning techniques. You need to explain the output of the model to the relevant stakeholders, such as engineers and maintenance teams, so they can take appropriate action based on the model's predictions. How should you build the model to achieve accurate predictions and provide understandable explanations?
A
Use scikit-learn to build a tree-based model, and use SHAP values to explain the model output.
B
Use scikit-learn to build a tree-based model, and use partial dependence plots (PDP) to explain the model output.
C
Use TensorFlow to create a deep learning-based model, and use Integrated Gradients to explain the model output.
D
Use TensorFlow to create a deep learning-based model, and use the sampled Shapley method to explain the model output.
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