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?
Click on the arrows to vote for the correct answer
A. B. C. D.B
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.
https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine-learning-service/The Microsoft transparency principle for responsible AI requires that machine learning models be explainable, interpretable, and transparent. This means that users should be able to understand how the model arrived at its predictions and have the ability to audit the model's decision-making process.
To ensure that a machine learning model built using the automated machine learning user interface meets the Microsoft transparency principle, you should enable the Explain best model option, which is option B in the list of answers.
Enabling Explain best model will generate an explanation for how the model arrived at its predictions. The explanation will include which features had the most significant impact on the model's predictions and how the model arrived at its overall decision. This feature enables users to understand and interpret the model's predictions, making the model more transparent and interpretable.
The other options listed in the answer choices are not directly related to ensuring that the model meets the Microsoft transparency principle.
Option A, setting the validation type to Auto, determines the type of cross-validation to use when evaluating the model's performance.
Option C, setting the primary metric to accuracy, determines which metric the automated machine learning algorithm will optimize for when selecting the best model.
Option D, setting max concurrent iterations to 0, determines the maximum number of iterations that can run at the same time.
Therefore, enabling Explain best model is the best option for ensuring that a machine learning model meets the Microsoft transparency principle for responsible AI.