Error Analysis
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About
Error Analysis is a Responsible AI toolkit that helps you get a deeper understanding of machine learning model errors. It enables you to identify cohorts with higher error rates and diagnose the root causes behind these errors. The toolkit offers features such as evaluating cohorts, exploring predictions with interpretability features, and an interactive dashboard. It also includes methods like Decision Tree visualization and Error Heatmap to identify and diagnose errors effectively. Furthermore, it allows for data exploration, global and local explanations, and what-if analysis to understand the reasons behind the error rates so you can take corrective actions.
Platform
Task
Features
• error heatmap to investigate the impact of input features on error rate across cohorts
• decision tree visualization for discovering cohorts with high error rates
• interactive dashboard for quick identification of errors and diagnosis of root causes
• explore predictions using interpretability features
• evaluate cohorts to learn how errors distribute across different groups
• diagnose root causes by diving deeper into data and model characteristics
• visualize error rate distribution
• identify cohorts with high error rate versus benchmark
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