Learn how enabling the ‘Explain best model’ feature in automated machine learning fosters transparency, offering insights into model selection, aligning with Microsoft’s responsible AI principles.
Table of Contents
Question
You build a machine learning model by using the automated machine learning user interface (UI).You need to ensure that the model meets the Microsoft transparency principle for responsible AI.
What should you do?
A. Set Validation type to Auto.
B. Enable Explain best model.
C. Set Primary metric to accuracy.
D. Set Max concurrent iterations to 0.
Answer
B. Enable Explain best model.
Explanation
Enabling the “Explain best model” feature in automated machine learning allows for interpretability and transparency in model decisions. It provides insights into why a specific model was chosen as the best, enhancing the understanding of its functioning, alignment with the Microsoft transparency principle for responsible AI.
Model Explain Ability. Most businesses run on trust and being able to open the ML “black box” helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs.
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