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…

The correct answer is B. fairlearn.postprocessing.ThresholdOptimizer. fairlearn.postprocessing.ThresholdOptimizer is the correct choice when you cannot retrain the model. It is a post-processing algorithm that wraps an already-trained classifier and adjusts its decision thresholds to satisfy fairness constraints, requiring no retraining. Options…

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Question

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?

Options

Afairlearn.reductions.ExponentiatedGradient
Bfairlearn.postprocessing.ThresholdOptimizer
Cfairlearnpreprocessing.CorrelationRemover
Dfairlearn.reductions.GridSearch

How the community answered

(25 responses)

A
8% (2)
B
72% (18)
C
4% (1)
D
16% (4)

Explanation

fairlearn.postprocessing.ThresholdOptimizer is the correct choice when you cannot retrain the model. It is a post-processing algorithm that wraps an already-trained classifier and adjusts its decision thresholds to satisfy fairness constraints, requiring no retraining. Options A (ExponentiatedGradient) and D (GridSearch) are reduction-based in-processing methods that train multiple models and require full retraining. Option C (CorrelationRemover) is a preprocessing technique applied to training data before model training begins.

Topics

#Fairlearn#Model Fairness#Post-processing#Responsible AI

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