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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.
Select and Place:
Correct Answer:
Explanation/Reference:
Explanation:
Step 1: Add a Two-Class Support Vector Machine module to initialize the SVM classifier.
Step 2: Add a dataset to the experiment Step 3: Add a Split Data module to create training and test dataset.
To generate a set of feature scores requires that you have an already trained model, as well as a test dataset.
Step 4: Add a Permutation Feature Importance module and connect to the trained model and test dataset.
Step 5: Set the Metric for measuring performance property to Classification – Accuracy and then run the experiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector-machinehttps://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance
Missing something, you need a step to train model before you can add Permutation module
1. add data set
2. split data set
3. add model
4. train model
5. add permutation
6. run exp.
1) Add a dataset,
2) Add a split data module,
3) add a 2-Class SVM classifier, 3b) Add a trained model (missing in the question),
4) Add a permutation feature importance module
5) Set the metric for measuring performance.
https://gallery.azure.ai/Experiment/e2ccb5a5d9dc480489ba8ff0b7eb98ac