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Splunk Machine Learning Toolkit: Calibrating Density Function Algorithm

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In the anomaly detection process, density function algorithm outputs isOutlier field with values 0 (Normal) and 1 (Abnormal) for each data point depending on the KPI behavior and historical data:

  1. Is there anyway to calibrate the density function algorithm where the data point can show Normal, Warning and Critical zones based on the severity of the anomaly?
  2. How to output the probability densities of the data points and graph them like kernel distributions?
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