Exam Professional Machine Learning Engineer topic 1 question 229 discussion

It is to understand why model is making specific mistakes, so example-based explanation makes sense to me.

https://cloud.google.com/vertex-ai/docs/explainable-ai/overview#example-based "Improve your data or model: One of the core use cases for example-based explanations is helping you understand why your model made certain mistakes in its predictions, and using those insights to improve your data or model. [...] For example, suppose we have a model that classifies images as either a bird or a plane, and that it is misclassifying the following bird as a plane with high confidence. You can use Example-based explanations to retrieve similar images from the training set to figure out what is happening." Not A: Integrated Gradients is recommended for low-contrast images, such as X-rays https://cloud.google.com/vertex-ai/docs/explainable-ai/overview#compare-methods Not C: Cannot set Outlines for XRAI https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/visualizing-explanations

Improve your data or model: One of the core use cases for example-based explanations is helping you understand why your model made certain mistakes in its predictions, and using those insights to improve your data or model. https://cloud.google.com/vertex-ai/docs/explainable-ai/overview

My Answer: A According to this documentation: https://cloud.google.com/vertex-ai/docs/explainable-ai/visualization-settings This option A aligns with using Integrated Gradients, which is suitable for feature-based explanations. Setting the visualization type to PIXELS allows for per-pixel attribution, which can help in understanding the specific regions of the image influencing the model's decision. Additionally, setting the clip_percent_upperbound parameter to 95 helps in filtering out noise and focusing on areas of strong attribution, which is crucial for understanding mislabeled images with high confidence. Option C suggests using XRAI for feature-based explanations and setting the visualization type to OUTLINES, along with setting the polarity to positive. However, based on the provided documentation, XRAI is recommended to have its visualization type set to PIXELS, not OUTLINES.

Although Xrai could be an option, it doesn't not allow you to set those options, so only other answer is A https://cloud.google.com/vertex-ai/docs/explainable-ai/visualization-settings#visualization_options

Going with A Not c - For XRAI, Pixels is the default setting and shows areas of attribution. Outlines is not recommended for XRAI. https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/visualizing-explanations