As a professional working for a textile manufacturer, you've developed a machine learning model aimed at detecting and classifying fabric defects from high-resolution images captured at the production line's end. The model has been trained to achieve high recall, ensuring that most defects are identified. However, to build trust among quality control inspectors, it's crucial to not only detect defects but also to explain the classifier's decision-making process clearly. The production environment requires that the explanation method must be computationally efficient to handle the high volume of images processed daily and must provide interpretable results to non-technical inspectors. Which method should you employ to elucidate the rationale behind your classifier's predictions while meeting these constraints? (Choose two correct options if E is available) | Google Professional Machine Learning Engineer Quiz