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Explaining regression prediction with contributions of input variables

Julian Klauser (2010) Explaining regression prediction with contributions of input variables. EngD thesis.

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    The explanation of predictions, made by regression algorithms, is a vey important aspect of the discovery of patterns in data. In this thesis we extend an existing method for explanation in classification, developed by Štrumbelj and Kononenko, to regression. The base of the method comes from game theory and with the help of sampling, solves the problem of exponential time growth. We presented the basic concept of the method and after its successful implementation, we tested it with various regression algorithms on artificial domains. We analyzed the performance and efficiency of the presented method and compared algorithms according to the success of the prediction and the success of the explanation. Besides the existing visualisation of the prediction's explanation we created a new way of visualising the explanation models. We have also shown that our method can be extended, so that it is able to cleave to the context with partial compliance to attribute value distribution.

    Item Type: Thesis (EngD thesis)
    Keywords: machine learning, prediction explanation, regression, explanation visualisation, model visualisation, attribute contribution, explanation in context.
    Number of Pages: 64
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    prof. dr. Igor Kononenko237Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=50070&select=(ID=7532116)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 999
    Date Deposited: 19 Jan 2010 08:56
    Last Modified: 13 Aug 2011 00:36
    URI: http://eprints.fri.uni-lj.si/id/eprint/999

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