How can I ensure the accuracy of my realm scan results?

General
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I just completed a realm scan for a crucial project, but I’m worried about the accuracy of the results. I followed the standard procedure, but there were some unexpected discrepancies. How can I ensure that my realm scan results are accurate? Are there any additional steps or tools I should use to verify the data and avoid errors? Your insights would be greatly appreciated!

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The following are steps and tools you can use to verify your data and avoid errors:

  • Double-check configurations: Ensure that your scan configurations are set correctly. Misconfigurations can lead to inaccurate results.
  • Use multiple scans: Run multiple scans at different times to see if the results are consistent. This can help identify any anomalies that might be due to temporary issues.
  • Cross-verify with other tools: Use additional scanning tools to cross-verify your results. Tools like Ascalon Scan can be useful for security and compliance checks.
  • Data integrity checks: Implement data integrity checks to ensure that the data remains accurate and consistent across your database.
  • Regular maintenance: Perform regular maintenance scans to keep your database in top condition and to catch any discrepancies early.
  • Logging and documentation: Keep detailed logs of your scan configurations, findings, and any remediation actions taken. This can help in future assessments and compliance auditing.
  • Consult experts: If discrepancies persist, consider consulting with experts who can provide insights and possibly identify issues that might not be apparent.
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To ensure accurate Realm scan results, run regular scans, analyze data thoroughly, compare results over time, and sample large datasets carefully. Validate findings with real-world usage, optimize based on insights, use Realm Studio for visualization, and consider third-party tools for extra insights.

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Make sure there is variation in your goal; this seems like a very consistent number.

Verify that any information you are using as a feature will be available at inference time in production by checking for information leakage.

If you find that your target values are significantly autocorrelated over time, you should either use TimeSeriesSplit or avoid shuffling before train-test splitting.