Topic 1 Question 347
A company needs to develop a model that uses a machine learning (ML) model for risk analysis. An ML engineer needs to evaluate the contribution each feature of a training dataset makes to the prediction of the target variable before the ML engineer selects features.
How should the ML engineer predict the contribution of each feature?
Use the Amazon SageMaker Data Wrangler multicollinearity measurement features and the principal component analysis (PCA) algorithm to calculate the variance of the dataset along multiple directions in the feature space.
Use an Amazon SageMaker Data Wrangler quick model visualization to find feature importance scores that are between 0.5 and 1.
Use the Amazon SageMaker Data Wrangler bias report to identify potential biases in the data related to feature engineering.
Use an Amazon SageMaker Data Wrangler data flow to create and modify a data preparation pipeline. Manually add the feature scores.
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コメント(5)
B is the correct answer. The most effective way for ML engineers to assess the contribution that each feature in the training dataset makes to the prediction of the target variable is to use the B. Amazon SageMaker Data Wrangler Quick Model Visualization to find feature importance scores between 0.5 and 1. This method provides a quick visualization of the importance of each feature and can be used to make selection decisions.
Other selections
A uses Principal Component Analysis (PCA) to calculate variance, but does not directly assess the contribution of a feature. C identifies bias and is not suitable for assessing contribution. D is related to data preparation but is not a method to evaluate the contribution of a feature.
👍 4kupo7772024/10/01- 正解だと思う選択肢: D
Should be D.
👍 1CW01062024/09/15 - 正解だと思う選択肢: B
This approach directly addresses the need to evaluate the contribution of each feature to the prediction of the target variable by providing feature importance scores, which are crucial for understanding and selecting the most impactful features.
👍 1MultiCloudIronMan2024/09/22
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