Exam DP-100 topic 3 question 134 discussion

correct answer https://learn.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml#mitigation-algorithms

The question is asking for post-processing so you have to choose ThresholdOptimizer.

GPT-3.5 To mitigate the unfairness in machine learning models that produce unfair predictions across sensitive features, you can use the following post-processing technique and model type: Post-processing technique: Threshold optimizer technique Model type: Binary classification model The Threshold optimizer technique is a post-processing technique that adjusts the decision threshold of a binary classification model to achieve fairness. By selecting an appropriate threshold, you can balance the trade-off between false positives and false negatives, thereby mitigating unfairness in predictions across sensitive features. In this case, since the question specifically mentions unfair predictions across sensitive features, we can infer that the problem involves binary classification (predicting two classes) rather than regression (predicting continuous values) or time series forecasting (predicting future values over time).