机器学习模型的可解释性提升方法研究 -计算机科学与技术专业

摘要

人工智能技术的快速发展要求机器学习模型具有更高的可解释性。本文综述了模型可解释性的理论基础、提升技术、增强策略及实际应用案例，并分析了研究局限与未来趋势。文章从透明度和可理解性概念出发，介绍了评估模型可解释性的关键指标，并探讨了提升技术，包括非黑盒模型设计与黑盒模型解释方法。文章还提出了多种增强策略，如鲁棒特征提取、数据隐私与可解释性平衡等，以提升模型透明度和可理解性。通过实际案例分析，展示了可解释性在提升模型信任度和决策合理性方面的作用。最后，文章剖析了当前研究的局限性，并展望了未来方向，包括发展更通用的可解释性框架和强化模型内部机制的可透视性。本文的研究成果对推动机器学习模型的透明化和可理解化进程具有重要意义。

关键词：机器学习；可解释性；模型性能

RESEARCH ON INTERPRETABILITY IMPROVEMENT OF MACHINE LEARNING MODELS

ABSTRACT

The rapid development of artificial intelligence technology requires higher interpretability of machine learning models. This paper reviews the theoretical basis, enhancement techniques, enhancement strategies and practical application cases of model explainability, and analyzes the research limitations and future trends. Starting from the concepts of transparency and understandability, this paper introduces the key indicators to evaluate the interpretability of models, and discusses the enhancement techniques, including non-black box model design and black box model interpretation methods. In order to improve the transparency and understandability of the model, a variety of enhancement strategies are proposed, such as robust feature extraction, data privacy and interpretability balance. Through the analysis of practical cases, the role of explainability in improving model trust and decision rationality is demonstrated. Finally, the paper analyzes the limitations of the current research and looks forward to future directions, including the development of a more general interpretability fr amework and strengthening the perspectivity of the internal mechanisms of the model. The research results of this paper have important significance to promote the process of transparency and understanding of machine learning models.

KEY WORDS：Machine learning; Interpretability; Model performance

摘要 I

ABSTRACT II

第1章引言 2

第2章可解释性理论基础 3

2.1 透明度与可理解性概念 3

2.2 可解释性与模型性能的关系 3

2.3 可解释性评估指标 3

第3章可解释性提升技术 5

3.1 非黑盒模型设计 5

3.2 黑盒模型解释方法 5

第4章可解释性增强策略 7