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SHAP feature importance.

SHAP feature importance.

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Figure 2: The stages in a ML lifecycle from problem formulation to...
Figure 3: Pre-training bias results in Data Wrangler.
Figure 4: Post-training bias results in Studio Trials.
Figure 5: SHAP feature importance.
+1 Figure 6: Scaling of SHAP on 100,000 examples and measuring time (left)...
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud
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  • Sep 2021
Understanding the predictions made by machine learning (ML) models and their potential biases remains a challenging and labor-intensive task that depends on the application, the dataset, and the specific model. We present Amazon SageMaker Clarify, an explainability feature for Amazon SageMaker that launched in December 2020, providing insights into...
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... understand which features are affecting the model's predictions the most we look at the global SHAP values that are averaged across all examples after taking the absolute value. The three most important features are A61 (having very little savings), the loan amount, and credit duration, see Figure 5. ...
Context 2
... understand which features are affecting the model's predictions the most we look at the global SHAP values that are averaged across all examples after taking the absolute value. The three most important features are A61 (having very little savings), the loan amount, and credit duration, see Figure 5. ...