You create a binary classification model. You use the Fairlearn package to assess model fairness. You must eliminate the need to retrain the model. You need to implement the Fairlearn package. Which algorithm should you use?
It states that the need to retrain the model should not apply, you can "enforce" fairness in a Model in 3 ways, before the training (preprocessing), during the training (inprocessing) and after the training (postprocessing). The Corrolation Remover is a preprocessing technique, therefore you would need to retrain the model. The correct answer is B.
The correct answer is B. fairlearn.postprocessing.ThresholdOptimizer.
The ThresholdOptimizer algorithm in the Fairlearn package is a post-processing technique that you can use to adjust the threshold of a binary classification model’s predictions to improve fairness, without needing to retrain the model. This makes it a suitable choice given your requirement to eliminate the need to retrain the model. The other options listed are either preprocessing techniques or in-processing techniques which would require retraining of the model.
The ThresholdOptimizer algorithm allows you to adjust the decision threshold of the binary classification model to improve the balance between the accuracy of the model and the fairness of the model's predictions. This algorithm takes the original binary classification model as input and produces a new, fairer model without the need for retraining. Others require retraining the model.
The correct sequence of actions is:
B) Register each training file as a new datastore
D) Add a new parameter in the module indicating the path to the training file
E) Publish a training pipeline
C) Run the training pipeline by using the studio portal
Action A is not required as the path to the training file will be specified through the new parameter added in step 2.
B
https://docs.microsoft.com/en-us/training/modules/detect-mitigate-unfairness-models-with-azure-machine-learning/4-mitigate-with-fairlearn
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