Topic 1 Question 206
You are building a predictive maintenance model to preemptively detect part defects in bridges. You plan to use high definition images of the bridges as model inputs. You need to explain the output of the model to the relevant stakeholders so they can take appropriate action. How should you build the model?
Use scikit-learn to build a tree-based model, and use SHAP values to explain the model output.
Use scikit-learn to build a tree-based model, and use partial dependence plots (PDP) to explain the model output.
Use TensorFlow to create a deep learning-based model, and use Integrated Gradients to explain the model output.
Use TensorFlow to create a deep learning-based model, and use the sampled Shapley method to explain the model output.
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- 正解だと思う選択肢: C
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.
👍 1pikachu0072024/01/12
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