An exact counterfactual-example-based approach to tree-ensemble models
interpretability
- URL: http://arxiv.org/abs/2105.14820v1
- Date: Mon, 31 May 2021 09:32:46 GMT
- Title: An exact counterfactual-example-based approach to tree-ensemble models
interpretability
- Authors: Pierre Blanchart
- Abstract summary: High-performance models do not exhibit the necessary transparency to make their decisions fully understandable.
We could derive an exact geometrical characterisation of their decision regions under the form of a collection of multidimensional intervals.
An adaptation to reasoning on regression problems is also envisaged.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Explaining the decisions of machine learning models is becoming a necessity
in many areas where trust in ML models decision is key to their
accreditation/adoption. The ability to explain models decisions also allows to
provide diagnosis in addition to the model decision, which is highly valuable
in scenarios such as fault detection. Unfortunately, high-performance models do
not exhibit the necessary transparency to make their decisions fully
understandable. And the black-boxes approaches, which are used to explain such
model decisions, suffer from a lack of accuracy in tracing back the exact cause
of a model decision regarding a given input. Indeed, they do not have the
ability to explicitly describe the decision regions of the model around that
input, which is necessary to determine what influences the model towards one
decision or the other. We thus asked ourselves the question: is there a
category of high-performance models among the ones currently used for which we
could explicitly and exactly characterise the decision regions in the input
feature space using a geometrical characterisation? Surprisingly we came out
with a positive answer for any model that enters the category of tree ensemble
models, which encompasses a wide range of high-performance models such as
XGBoost, LightGBM, random forests ... We could derive an exact geometrical
characterisation of their decision regions under the form of a collection of
multidimensional intervals. This characterisation makes it straightforward to
compute the optimal counterfactual (CF) example associated with a query point.
We demonstrate several possibilities of the approach, such as computing the CF
example based only on a subset of features. This allows to obtain more
plausible explanations by adding prior knowledge about which variables the user
can control. An adaptation to CF reasoning on regression problems is also
envisaged.
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