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…

Scikit-learn tree-based models are generally not suitable for direct processing of high-definition image inputs without significant and complex feature engineering, making this model choice inappropriate for the given input data type.

Scikit-learn tree-based models are not designed for direct input of high-definition images, and while Partial Dependence Plots (PDP) are a general explainability technique, the choice of model is fundamentally inappropriate for image data.

For a predictive maintenance model using high-definition images as input, a deep learning-based model created with TensorFlow is the appropriate choice due to its strength in image processing. Integrated Gradients is a powerful and suitable explainability technique for deep neural networks on image data, allowing stakeholders to understand which parts of an image contributed most to a defect prediction.

While TensorFlow and deep learning are appropriate for image data, the Sampled Shapley method can be computationally very expensive for deep learning models on image data. Integrated Gradients is often a more practical and effective technique specifically designed for attributing feature importance in differentiable models like neural networks.