You are a data scientist working for a bank and have used Azure ML to train and register a machine learning model that predicts whether a customer is likely to repay a loan. You want to understand…

Question

Options

• AEnable data drift monitoring for the model and its training dataset.
• BScore the model against some test data with known label values and use the results to calculate
• CUse the Hyperdrive library to test the model with multiple hyperparameter values.
• DUse the interpretability package to generate an explainer for the model.
• EAdd tags to the model registration indicating the names of the features in the training dataset.

• A
  10% (2)
• B
  15% (3)
• C
  5% (1)
• D
  70% (14)

Explanation

When you compute model explanations and visualize them, you're not limited to an existing model explanation for an automated ML model. You can also get an explanation for your model with different test data. The steps in this section show you how to compute and visualize engineered feature importance based on your test data. Incorrect Answers: A: In the context of machine learning, data drift is the change in model input data that leads to model performance degradation. It is one of the top reasons where model accuracy degrades over time, thus monitoring data drift helps detect model performance issues. B: A confusion matrix is used to describe the performance of a classification model. Each row displays the instances of the true, or actual class in your dataset, and each column represents the instances of the class that was predicted by the model. C: Hyperparameters are adjustable parameters you choose for model training that guide the training process. The HyperDrive package helps you automate choosing these parameters. https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning- interpretability-automl

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