DRAG DROP
You have several machine learning models registered in an Azure Machine Learning workspace.
You must use the Fairlearn dashboard to assess fairness in a selected model.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Graphical user interface, text, application
Description automatically generated
Step 1: Select a model feature to be evaluated.
Step 2: Select a binary classification or regression model.
Register your models within Azure Machine Learning. For convenience, store the results in a dictionary, which maps the id of the registered model (a string in name:version format) to the predictor itself.
Example:
model_dict = {}
lr_reg_id = register_model("fairness_logistic_regression", lr_predictor)
model_dict[lr_reg_id] = lr_predictor
svm_reg_id = register_model("fairness_svm", svm_predictor)
model_dict[svm_reg_id] = svm_predictor
Step 3: Select a metric to be measured
Precompute fairness metrics.
Create a dashboard dictionary using Fairlearn’s metrics package.
Latest DP-100 Dumps Valid Version with 227 Q&As
Latest And Valid Q&A | Instant Download | Once Fail, Full Refund