Exam Professional Machine Learning Engineer topic 1 question 235 discussion

My Answer: D This approach leverages Vertex Explainable AI to provide feature attributions, which helps in understanding the rationale behind the model's decisions. By aggregating these feature attributions over the entire dataset, you can gain insights into potential biases or areas of concern. Analyzing these results alongside standard model evaluation metrics allows for a comprehensive understanding of the model's performance and its interpretability. Option C is better to understand specific cases, but does not show overall contributions.

agree with guilhermebutzke

Debugging models: Feature attributions can help detect issues in the data that standard model evaluation techniques would usually miss.

If you inspect specific instances, and also aggregate feature attributions across your training dataset, you can get deeper insight into how your model works. Consider the following advantages: Debugging models: Feature attributions can help detect issues in the data that standard model evaluation techniques would usually miss. https://cloud.google.com/vertex-ai/docs/explainable-ai/overview

C. Example-based explanations make more sense in this case than feature based attributions (we want to understand with examples what kind of decisions the model takes; also explore the amount of bias in a visual, understandable way) https://cloud.google.com/vertex-ai/docs/explainable-ai/overview#example-based

Feature-Level Insights: Feature attributions pinpoint which image regions contribute most to predictions, offering granular understanding of model reasoning. Bias Detection: Aggregating feature attributions over the entire dataset can reveal systematic biases or patterns of model behavior, helping identify potential fairness issues. Complementary to Evaluation Metrics: Combining attributions with standard metrics (e.g., accuracy, precision, recall) provides a comprehensive view of model performance and fairness.