Inherently Interpretable Tree Ensemble Learning

• URL: http://arxiv.org/abs/2410.19098v1
• Date: Thu, 24 Oct 2024 18:58:41 GMT
• Title: Inherently Interpretable Tree Ensemble Learning
• Authors: Zebin Yang, Agus Sudjianto, Xiaoming Li, Aijun Zhang,
• Abstract summary: We show that when shallow decision trees are used as base learners, the ensemble learning algorithms can become inherently interpretable. An interpretation algorithm is developed that converts the tree ensemble into the functional ANOVA representation with inherent interpretability. Experiments on simulations and real-world datasets show that our proposed methods offer a better trade-off between model interpretation and predictive performance.
• Score: 7.868733904112288
• License:
• Abstract: Tree ensemble models like random forests and gradient boosting machines are widely used in machine learning due to their excellent predictive performance. However, a high-performance ensemble consisting of a large number of decision trees lacks sufficient transparency and explainability. In this paper, we demonstrate that when shallow decision trees are used as base learners, the ensemble learning algorithms can not only become inherently interpretable subject to an equivalent representation as the generalized additive models but also sometimes lead to better generalization performance. First, an interpretation algorithm is developed that converts the tree ensemble into the functional ANOVA representation with inherent interpretability. Second, two strategies are proposed to further enhance the model interpretability, i.e., by adding constraints in the model training stage and post-hoc effect pruning. Experiments on simulations and real-world datasets show that our proposed methods offer a better trade-off between model interpretation and predictive performance, compared with its counterpart benchmarks.

Related papers

• Improving Network Interpretability via Explanation Consistency Evaluation [56.14036428778861]
  We propose a framework that acquires more explainable activation heatmaps and simultaneously increase the model performance. Specifically, our framework introduces a new metric, i.e., explanation consistency, to reweight the training samples adaptively in model learning. Our framework then promotes the model learning by paying closer attention to those training samples with a high difference in explanations.
  arXiv  Detail & Related papers  (2024-08-08T17:20:08Z)
• Modern Neighborhood Components Analysis: A Deep Tabular Baseline Two Decades Later [59.88557193062348]
  We revisit the classic Neighborhood Component Analysis (NCA), designed to learn a linear projection that captures semantic similarities between instances. We find that minor modifications, such as adjustments to the learning objectives and the integration of deep learning architectures, significantly enhance NCA's performance. We also introduce a neighbor sampling strategy that improves both the efficiency and predictive accuracy of our proposed ModernNCA.
  arXiv  Detail & Related papers  (2024-07-03T16:38:57Z)
• Unboxing Tree Ensembles for interpretability: a hierarchical visualization tool and a multivariate optimal re-built tree [0.34530027457862006]
  We develop an interpretable representation of a tree-ensemble model that can provide valuable insights into its behavior. The proposed model is effective in yielding a shallow interpretable tree approxing the tree-ensemble decision function.
  arXiv  Detail & Related papers  (2023-02-15T10:43:31Z)
• Concept-based Explanations using Non-negative Concept Activation Vectors and Decision Tree for CNN Models [4.452019519213712]
  This paper evaluates whether training a decision tree based on concepts extracted from a concept-based explainer can increase interpretability for Convolutional Neural Networks (CNNs) models.
  arXiv  Detail & Related papers  (2022-11-19T21:42:55Z)
• A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds [9.546094657606178]
  We study the generalization performance of decision trees with respect to different generative regression models. This allows us to elicit their inductive bias, that is, the assumptions the algorithms make (or do not make) to generalize to new data. We prove a sharp squared error generalization lower bound for a large class of decision tree algorithms fitted to sparse additive models.
  arXiv  Detail & Related papers  (2021-10-18T21:22:40Z)
• Sparse MoEs meet Efficient Ensembles [49.313497379189315]
  We study the interplay of two popular classes of such models: ensembles of neural networks and sparse mixture of experts (sparse MoEs) We present Efficient Ensemble of Experts (E$3$), a scalable and simple ensemble of sparse MoEs that takes the best of both classes of models, while using up to 45% fewer FLOPs than a deep ensemble.
  arXiv  Detail & Related papers  (2021-10-07T11:58:35Z)
• Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation [89.38054401427173]
  Aspect-based Sentiment Analysis (ABSA) seeks to predict the sentiment polarity of a sentence toward a specific aspect. dependency trees can be integrated into deep learning models to produce the state-of-the-art performance for ABSA. We propose a novel graph-based deep learning model to overcome these two issues.
  arXiv  Detail & Related papers  (2020-10-26T07:36:24Z)
• Surrogate Locally-Interpretable Models with Supervised Machine Learning Algorithms [8.949704905866888]
  Supervised Machine Learning algorithms have become popular in recent years due to their superior predictive performance over traditional statistical methods. The main focus is on interpretability, the resulting surrogate model also has reasonably good predictive performance.
  arXiv  Detail & Related papers  (2020-07-28T23:46:16Z)
• Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach [78.77265671634454]
  We make use of a multi-task objective, i.e., the models simultaneously predict words as well as ground truth parse trees in a form called "syntactic distances" Experimental results on the Penn Treebank and Chinese Treebank datasets show that when ground truth parse trees are provided as additional training signals, the model is able to achieve lower perplexity and induce trees with better quality.
  arXiv  Detail & Related papers  (2020-05-12T15:35:00Z)
• Interpretable Learning-to-Rank with Generalized Additive Models [78.42800966500374]
  Interpretability of learning-to-rank models is a crucial yet relatively under-examined research area. Recent progress on interpretable ranking models largely focuses on generating post-hoc explanations for existing black-box ranking models. We lay the groundwork for intrinsically interpretable learning-to-rank by introducing generalized additive models (GAMs) into ranking tasks.
  arXiv  Detail & Related papers  (2020-05-06T01:51:30Z)
• Interpretable MTL from Heterogeneous Domains using Boosted Tree [8.095372074268685]
  Multi-task learning (MTL) aims at improving the generalization performance of several related tasks. In this paper, following the philosophy of boosted tree, we proposed a two-stage method. Experiments on both benchmark and real-world datasets validate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2020-03-16T08:58:51Z)

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.