Interpreting random forest models using a feature contribution method

@article{Palczewska2013InterpretingRF,
  title={Interpreting random forest models using a feature contribution method},
  author={A. Palczewska and Jan Palczewski and R. Robinson and D. Neagu},
  journal={2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)},
  year={2013},
  pages={112-119}
}
  • A. Palczewska, Jan Palczewski, +1 author D. Neagu
  • Published 2013
  • Computer Science
  • 2013 IEEE 14th International Conference on Information Reuse & Integration (IRI)
  • Model interpretation is one of the key aspects of the model evaluation process. [...] Key Result The robustness of results is demonstrated through an extensive analysis of feature contributions calculated for a large number of generated random forest models.Expand Abstract
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    References

    SHOWING 1-10 OF 11 REFERENCES
    Interpretation of QSAR Models Based on Random Forest Methods
    • 46
    Random Forests
    • L. Breiman
    • Mathematics, Computer Science
    • Machine Learning
    • 2004
    • 50,768
    • PDF
    Classification and regression trees
    • W. Loh
    • Computer Science
    • Wiley Interdiscip. Rev. Data Min. Knowl. Discov.
    • 2011
    • 14,038
    • PDF
    Classification and Regression by randomForest
    • 10,510
    • PDF
    How to Explain Individual Classification Decisions
    • 541
    • PDF
    Classification and regression by randomforest. R News
    • Classification and regression by randomforest. R News
    • 2002
    Visual interpretation of kernelbased prediction models
    • Classification and regression trees
    • 1984
    Breast Cancer Wisconsin Diagnostic dataset
    • Breast Cancer Wisconsin Diagnostic dataset