Q: 23

DRAG DROP You have a dataset that contains over 150 features. You use the dataset to train a Support Vector Machine (SVM) binary classifier. You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of feature importance scores for the dataset. In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

Drag & Drop

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Add a Two-Class Support Vector Machine module to initialize the SVM classifier.
Set the Metric for measuring performance property to Classification - Accuracy and then run the experiment.
Add a Permutation Feature Importance module and connect the trained model and test dataset.
Add a dataset to the experiment.
Add a Split Data module to create training and test datasets.

Correct Answer:

Answer Areas Target 1: D. Add a dataset to the experiment. Target 2: E. Add a Split Data module to create training and test datasets. Target 3: A. Add a Two-Class Support Vector Machine module to initialize the SVM classifier. Target 4: C. Add a Permutation Feature Importance module and connect the trained model and test dataset. Target 5: B. Set the Metric for measuring performance property to Classification - Accuracy and then run the experiment.

Explanation

The Permutation Feature Importance (PFI) module requires two specific inputs: a trained model and a test dataset. Therefore, the workflow must follow the standard machine learning pipeline order:

Data Ingestion: Start by adding the dataset.
Data Preparation: Use the Split Data module to separate the data into training and test sets (the test set is required for the PFI module).
Model Initialization: Add the Two-Class Support Vector Machine module to define the algorithm (which creates the model to be trained).
PFI Setup: Once the model and data prerequisites are in place, add the Permutation Feature Importance module and connect the trained model and the test dataset.
Execution: Finally, configure the specific metric (Accuracy) and run the experiment to compute the scores.

References

Microsoft. (n.d.). Permutation Feature Importance component. Azure Machine Learning. Retrieved from https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/permutation-feature-importance

Section: "How to use Permutation Feature Importance"

Step 2: "Connect a trained model to the left input."

Step 3: "Connect a test dataset to the right input."

Step 4: "In the Metric for measuring performance, select the metric to use..."

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