Outlier detection algorithms comparison

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  yyhuang Administrator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 364 RM Data Scientist

  March 2019 Solution Accepted

  Hi @zzM,
  
  It is not possible to get the performance of unsupervised outlier detection, if we have no label for the ground truth.
  
  Check out this research paper for a comprehensive overview of the anomaly detection models which are available in anomaly detection extension 
  
  YY

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  Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,635 Unicorn

  March 2019 Solution Accepted

  Without a binary classification problem that has a priori answers (the label) to which you are comparing a prediction (the score), it is not possible to produce the ROC/AUC performance metric.  So the only way to produce that would be to separately label all cases as to whether they were in fact outliers in your opinion based on whatever criteria you are using, and then treat the output from the different outlier algorithms as though they were predictive models.  This is the main difference between supervised and unsupervised machine learning problems, which is what @yyhuang was talking about before.  So the short answer to your question is "not unless you dramatically change the nature of the problem."

  Brian T.
  Lindon Ventures 
  Data Science Consulting from Certified RapidMiner Experts

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