Topic 1 Question 227
3 つ選択A company has hired a data scientist to create a loan risk model. The dataset contains loan amounts and variables such as loan type, region, and other demographic variables. The data scientist wants to use Amazon SageMaker to test bias regarding the loan amount distribution with respect to some of these categorical variables.
Which pretraining bias metrics should the data scientist use to check the bias distribution?
Class imbalance
Conditional demographic disparity
Difference in proportions of labels
Jensen-Shannon divergence
Kullback-Leibler divergence
Total variation distance
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Agree with austinoy, answer should be DEF.
👍 3jackzhao2023/03/22- 正解だと思う選択肢: ABC
D. Jensen-Shannon divergence and E. Kullback-Leibler divergence are post-training bias metrics that measure the distance between two probability distributions. They are not pretraining bias metrics and cannot be used to check the bias distribution of the dataset.
F. Total variation distance is a post-training bias metric that measures the difference between two probability distributions. It is not a pretraining bias metric and cannot be used to check the bias distribution of the dataset.
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👍 2Mllb2023/04/01 - 正解だと思う選択肢: DEF👍 2Ahmedhadi_2023/04/25
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