Anomalies detection in prognostic data analysis

Designing data models for prognostic purposes require anomalies detection method. This article describes the choice of the method and how it applies for the decision tree model algorithm. The authors not only describe the methods of data anomalies search, but also explain basic steps of the algorithm itself. The work analyzes search parameters and their major influence on the method application outcome. As a result of both anomalies detection methods and decision tree model algorithm design the accuracy of the prognostic model increases. It happens due to improved model robustness and also a significant performance improvement of the analysis.

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