How to use Machine Learning for Anomaly Detection and Conditional Monitoring

  • The main goal of Anomaly Detection analysis is to identify the observations that do not adhere to general patterns considered as normal behavior.
  • Anomaly Detection could be useful in understanding data problems.
  • There are domains where anomaly detection methods are quite effective.
  • Modern ML tools include Isolation Forests and other similar methods, but you need to understand the basic concept for successful implementation
  • Isolation Forests method is unsupervised outlier detection method with interpretable results.

Introduction

What is Anomaly Detection? Practical use cases.

  • data errors (measurement inaccuracies, rounding, incorrect writing, etc.);
  • noise data points;
  • hidden patterns in the dataset (fraud or attack requests).
  • Supervised methods;
  • Unsupervised methods.

Unsupervised Anomaly Detection using Isolation Forests

Conclusion

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Founder & CEO of Education Ecosystem. Serial entrepreneur with experience from Amazon, GE & Rebate Networks, https://www.education-ecosystem.com/

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Dr. Michael J. Garbade

Dr. Michael J. Garbade

Founder & CEO of Education Ecosystem. Serial entrepreneur with experience from Amazon, GE & Rebate Networks, https://www.education-ecosystem.com/

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