ExplainPrediction-package: Explanation of individual predictions and models

Description Details Author(s) References See Also


The package ExplainPrediction contains methods to generate explanations for individual predictions of classification and regression models.


The explanation methodology used is based on measuring contributions of individual features on an individual predictions. The contributions of all attributes present an explanation of individual prediction. Explanations can be visualized with a nomogram. If we average the explanations, we get an explanation of the whole model. Two explanation methods are implemented:

Currently prediction models implemented in package CORElearn are supported, for other models a wrapper of class CoreModel has to be created. The wrapper has to present the model with a list with the following components:

Additionally it has to implement function predict which returns the same components as the function predict.CoreModel in the package CORElearn.

Further software and development versions of the package are available at http://lkm.fri.uni-lj.si/rmarko/software.


Marko Robnik-Sikonja


Marko Robnik-Sikonja, Igor Kononenko: Explaining Classifications For Individual Instances. IEEE Transactions on Knowledge and Data Engineering, 20:589-600, 2008

Erik Strumbelj, Igor Kononenko, Igor, Marko Robnik-Sikonja: Explaining Instance Classifications with Interactions of Subsets of Feature Values. Data and Knowledge Engineering, 68(10):886-904, Oct. 2009

Erik Strumbelj, Igor Kononenko: An Eficient Explanation of Individual Classifications using Game Theory, Journal of Machine Learning Research, 11(1):1-18, 2010.

Some references are available from http://lkm.fri.uni-lj.si/rmarko/papers/

See Also


ExplainPrediction documentation built on May 29, 2017, 3:39 p.m.

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