Optimal Counterfactual Explanations in Tree Ensembles
- URL: http://arxiv.org/abs/2106.06631v1
- Date: Fri, 11 Jun 2021 22:44:27 GMT
- Title: Optimal Counterfactual Explanations in Tree Ensembles
- Authors: Axel Parmentier, Thibaut Vidal
- Abstract summary: We advocate for a model-based search aiming at "optimal" explanations and propose efficient mixed-integer programming approaches.
We show that isolation forests can be modeled within our framework to focus the search on plausible explanations with a low outlier score.
- Score: 3.8073142980733
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Counterfactual explanations are usually generated through heuristics that are
sensitive to the search's initial conditions. The absence of guarantees of
performance and robustness hinders trustworthiness. In this paper, we take a
disciplined approach towards counterfactual explanations for tree ensembles. We
advocate for a model-based search aiming at "optimal" explanations and propose
efficient mixed-integer programming approaches. We show that isolation forests
can be modeled within our framework to focus the search on plausible
explanations with a low outlier score. We provide comprehensive coverage of
additional constraints that model important objectives, heterogeneous data
types, structural constraints on the feature space, along with resource and
actionability restrictions. Our experimental analyses demonstrate that the
proposed search approach requires a computational effort that is orders of
magnitude smaller than previous mathematical programming algorithms. It scales
up to large data sets and tree ensembles, where it provides, within seconds,
systematic explanations grounded on well-defined models solved to optimality.
Related papers
- BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving [11.596474985695679]
We release the StructuredOR dataset, annotated with comprehensive labels that capture the complete mathematical modeling process.
We propose BPP-Search, a algorithm that integrates reinforcement learning into a tree-of-thought structure.
BPP-Search significantly outperforms state-of-the-art methods, including Chain-of-Thought, Self-Consistency, and Tree-of-Thought.
arXiv Detail & Related papers (2024-11-26T13:05:53Z) - Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations [80.86128012438834]
We show for the first time that computing the robustness of counterfactuals with respect to plausible model shifts is NP-complete.
We propose a novel probabilistic approach which is able to provide tight estimates of robustness with strong guarantees.
arXiv Detail & Related papers (2024-07-10T09:13:11Z) - Tree ensemble kernels for Bayesian optimization with known constraints
over mixed-feature spaces [54.58348769621782]
Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search.
Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function.
Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.
arXiv Detail & Related papers (2022-07-02T16:59:37Z) - Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner [56.08919422452905]
We propose an architecture called Iterative Retrieval-Generation Reasoner (IRGR)
Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises.
We outperform existing benchmarks on premise retrieval and entailment tree generation, with around 300% gain in overall correctness.
arXiv Detail & Related papers (2022-05-18T21:52:11Z) - Multi-objective Explanations of GNN Predictions [15.563499097282978]
Graph Neural Network (GNN) has achieved state-of-the-art performance in various high-stake prediction tasks.
Prior methods use simpler subgraphs to simulate the full model, or counterfactuals to identify the causes of a prediction.
arXiv Detail & Related papers (2021-11-29T16:08:03Z) - Parsimonious Inference [0.0]
Parsimonious inference is an information-theoretic formulation of inference over arbitrary architectures.
Our approaches combine efficient encodings with prudent sampling strategies to construct predictive ensembles without cross-validation.
arXiv Detail & Related papers (2021-03-03T04:13:14Z) - Learning Optimal Tree Models Under Beam Search [27.92120639502327]
Existing tree models suffer from the training-testing discrepancy.
We develop the concept of Bayes optimality under beam search and calibration under beam search.
We propose a novel algorithm for learning optimal tree models under beam search.
arXiv Detail & Related papers (2020-06-27T17:20:04Z) - Evaluations and Methods for Explanation through Robustness Analysis [117.7235152610957]
We establish a novel set of evaluation criteria for such feature based explanations by analysis.
We obtain new explanations that are loosely necessary and sufficient for a prediction.
We extend the explanation to extract the set of features that would move the current prediction to a target class.
arXiv Detail & Related papers (2020-05-31T05:52:05Z) - ENTMOOT: A Framework for Optimization over Ensemble Tree Models [57.98561336670884]
ENTMOOT is a framework for integrating tree models into larger optimization problems.
We show how ENTMOOT allows a simple integration of tree models into decision-making and black-box optimization.
arXiv Detail & Related papers (2020-03-10T14:34:07Z) - A General Framework for Consistent Structured Prediction with Implicit
Loss Embeddings [113.15416137912399]
We propose and analyze a novel theoretical and algorithmic framework for structured prediction.
We study a large class of loss functions that implicitly defines a suitable geometry on the problem.
When dealing with output spaces with infinite cardinality, a suitable implicit formulation of the estimator is shown to be crucial.
arXiv Detail & Related papers (2020-02-13T10:30:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.