MLS-C01 · Question #374
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…
The correct answer is B. Use an Amazon SageMaker Data Wrangler quick model visualization to find feature importance. To evaluate the contribution of each feature to the prediction of the target variable, an ML engineer should use Amazon SageMaker Data Wrangler's quick model visualization for feature importance.
Question
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?
Options
- AUse the Amazon SageMaker Data Wrangler multicollinearity measurement features and the
- BUse an Amazon SageMaker Data Wrangler quick model visualization to find feature importance
- CUse the Amazon SageMaker Data Wrangler bias report to identify potential biases in the data
- DUse an Amazon SageMaker Data Wrangler data flow to create and modify a data preparation
How the community answered
(21 responses)- A14% (3)
- B76% (16)
- C5% (1)
- D5% (1)
Why each option
To evaluate the contribution of each feature to the prediction of the target variable, an ML engineer should use Amazon SageMaker Data Wrangler's quick model visualization for feature importance.
Multicollinearity measurement identifies correlations between independent variables, not their direct contribution to the prediction of the target variable, which is a different aspect of feature evaluation.
Amazon SageMaker Data Wrangler provides quick model visualizations that can generate a feature importance score for each feature. This score quantifies the contribution each feature makes to the model's prediction of the target variable, directly addressing the requirement to evaluate feature contributions before selection.
A bias report from Amazon SageMaker Data Wrangler is used to identify potential biases within the dataset or model, which is distinct from evaluating a feature's predictive contribution.
An Amazon SageMaker Data Wrangler data flow is a pipeline for data preparation steps like ingestion, transformations, and cleaning, not a tool for analyzing feature contributions to a target variable's prediction.
Concept tested: SageMaker Data Wrangler Feature Importance
Source: https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-analyze.html
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