Robustness is a measure how sensitive a model is tosmaller or larger deviations of the input from theassumptions the model relies on, or the distribu-tion observed in the train data. These deviationsare called outliers and may considerably degradethe performance of the predictive models. Classicalrobust statistics introduces a range of measures ofrobustness: breakdown value, sensitivity curve, in-fluence function, maxbias curve, etc.
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Student Target Groups:
- Students in ICE and Computer Science.)
Thesis Type:
- Master Project / Master Thesis
Goals and tasks:
- Literature review on robust statistics and itsapplication in the context of outlier detectionand analysis of predictive models.
- Robustness analysis of several models operat-ing on various data sets.
- Extend robustness measures to improve ro-bustness of predictive models and to detectoutliers in existing data.
- Integration of the solution into an existingcloud-based service.
- Summary of the results in a written report,oral presentation.
Recommended Prior Knowledge:
- A good knowledge of data analysis methodsand statistics, enjoy working with real data
- Interest to learn robust statistics (no priorknowledge is required), creative thinking.
- Programming skills in Python.
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