Forest-ORE: Mining Optimal Rule Ensemble to interpret Random Forest models

• URL: http://arxiv.org/abs/2403.17588v1
• Date: Tue, 26 Mar 2024 10:54:07 GMT
• Title: Forest-ORE: Mining Optimal Rule Ensemble to interpret Random Forest models
• Authors: Haddouchi Maissae, Berrado Abdelaziz,
• Abstract summary: We present Forest-ORE, a method that makes Random Forest (RF) interpretable via an optimized rule ensemble (ORE) for local and global interpretation. A comparative analysis of well-known methods shows that Forest-ORE provides an excellent trade-off between predictive performance, interpretability coverage, and model size.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Random Forest (RF) is well-known as an efficient ensemble learning method in terms of predictive performance. It is also considered a Black Box because of its hundreds of deep decision trees. This lack of interpretability can be a real drawback for acceptance of RF models in several real-world applications, especially those affecting one's lives, such as in healthcare, security, and law. In this work, we present Forest-ORE, a method that makes RF interpretable via an optimized rule ensemble (ORE) for local and global interpretation. Unlike other rule-based approaches aiming at interpreting the RF model, this method simultaneously considers several parameters that influence the choice of an interpretable rule ensemble. Existing methods often prioritize predictive performance over interpretability coverage and do not provide information about existing overlaps or interactions between rules. Forest-ORE uses a mixed-integer optimization program to build an ORE that considers the trade-off between predictive performance, interpretability coverage, and model size (size of the rule ensemble, rule lengths, and rule overlaps). In addition to providing an ORE competitive in predictive performance with RF, this method enriches the ORE through other rules that afford complementary information. It also enables monitoring of the rule selection process and delivers various metrics that can be used to generate a graphical representation of the final model. This framework is illustrated through an example, and its robustness is assessed through 36 benchmark datasets. A comparative analysis of well-known methods shows that Forest-ORE provides an excellent trade-off between predictive performance, interpretability coverage, and model size.

Related papers

• A survey and taxonomy of methods interpreting random forest models [0.0]
  The interpretability of random forest (RF) models is a research topic of growing interest in the machine learning (ML) community. RF resulting model is regarded as a "black box" because of its numerous deep decision trees. This paper aims to provide an extensive review of methods used in the literature to interpret RF resulting models.
  arXiv  Detail & Related papers  (2024-07-17T17:33:32Z)
• A Unified Approach to Extract Interpretable Rules from Tree Ensembles via Integer Programming [2.1408617023874443]
  Tree ensemble methods are known for their effectiveness in supervised classification and regression tasks. Our work aims to extract an optimized list of rules from a trained tree ensemble, providing the user with a condensed, interpretable model.
  arXiv  Detail & Related papers  (2024-06-30T22:33:47Z)
• Likelihood Ratio Confidence Sets for Sequential Decision Making [51.66638486226482]
  We revisit the likelihood-based inference principle and propose to use likelihood ratios to construct valid confidence sequences. Our method is especially suitable for problems with well-specified likelihoods. We show how to provably choose the best sequence of estimators and shed light on connections to online convex optimization.
  arXiv  Detail & Related papers  (2023-11-08T00:10:21Z)
• Obtaining Explainable Classification Models using Distributionally Robust Optimization [12.511155426574563]
  We study generalized linear models constructed using sets of feature value rules. An inherent trade-off exists between rule set sparsity and its prediction accuracy. We propose a new formulation to learn an ensemble of rule sets that simultaneously addresses these competing factors.
  arXiv  Detail & Related papers  (2023-11-03T15:45:34Z)
• Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactuals [67.64770842323966]
  Causal explanations of predictions of NLP systems are essential to ensure safety and establish trust. Existing methods often fall short of explaining model predictions effectively or efficiently. We propose two approaches for counterfactual (CF) approximation.
  arXiv  Detail & Related papers  (2023-10-01T07:31:04Z)
• Training and Inference on Any-Order Autoregressive Models the Right Way [97.39464776373902]
  A family of Any-Order Autoregressive Models (AO-ARMs) has shown breakthrough performance in arbitrary conditional tasks. We identify significant improvements to be made to previous formulations of AO-ARMs. Our method leads to improved performance with no compromises on tractability.
  arXiv  Detail & Related papers  (2022-05-26T18:00:02Z)
• Better Short than Greedy: Interpretable Models through Optimal Rule Boosting [10.938624307941197]
  Rule ensembles are designed to provide a useful trade-off between predictive accuracy and model interpretability. We present a novel approach aiming to fit rule ensembles of maximal predictive power for a given ensemble size.
  arXiv  Detail & Related papers  (2021-01-21T01:03:48Z)
• Autoregressive Score Matching [113.4502004812927]
  We propose autoregressive conditional score models (AR-CSM) where we parameterize the joint distribution in terms of the derivatives of univariable log-conditionals (scores) For AR-CSM models, this divergence between data and model distributions can be computed and optimized efficiently, requiring no expensive sampling or adversarial training. We show with extensive experimental results that it can be applied to density estimation on synthetic data, image generation, image denoising, and training latent variable models with implicit encoders.
  arXiv  Detail & Related papers  (2020-10-24T07:01:24Z)
• Interpretation and Simplification of Deep Forest [4.576379639081977]
  We consider quantifying the feature contributions and frequency of the fully trained deep RF in the form of a decision rule set. Model simplification is achieved by eliminating unnecessary rules by measuring the feature contributions. Experiment results have shown that a feature contribution analysis allows a black box model to be decomposed for quantitatively interpreting a rule set.
  arXiv  Detail & Related papers  (2020-01-14T11:30:26Z)
• Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation [77.7420231319632]
  We adapt contextual generation of categorical sequences to a policy gradient estimator, which evaluates a set of correlated Monte Carlo (MC) rollouts for variance control. We also demonstrate the use of correlated MC rollouts for binary-tree softmax models, which reduce the high generation cost in large vocabulary scenarios.
  arXiv  Detail & Related papers  (2019-12-31T03:01:55Z)
• Polynomial-Time Exact MAP Inference on Discrete Models with Global Dependencies [83.05591911173332]
  junction tree algorithm is the most general solution for exact MAP inference with run-time guarantees. We propose a new graph transformation technique via node cloning which ensures a run-time for solving our target problem independently of the form of a corresponding clique tree.
  arXiv  Detail & Related papers  (2019-12-27T13:30:29Z)

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.