论文标题
可解释的人工智能的R包装的景观
Landscape of R packages for eXplainable Artificial Intelligence
论文作者
论文摘要
数据和计算能力的可用性不断增长，为预测模型的发展提供了发展。为了确保此类模型的安全有效运行，我们需要探索，调试和验证的方法。为此目的的新方法和工具正在机器学习的可解释的人工智能（XAI）子域中开发。在这项工作（1）中，我们介绍了模型解释方法的分类法，（2）我们识别并比较了R中可用于执行XAI分析的27个软件包，（3）我们提供了适用特定软件包的示例，（4）我们承认XAI的最新趋势。该文章主要专门用于R中可用的工具，但是由于集成Python代码很容易，因此我们还将展示Python最受欢迎的库的示例。
The growing availability of data and computing power fuels the development of predictive models. In order to ensure the safe and effective functioning of such models, we need methods for exploration, debugging, and validation. New methods and tools for this purpose are being developed within the eXplainable Artificial Intelligence (XAI) subdomain of machine learning. In this work (1) we present the taxonomy of methods for model explanations, (2) we identify and compare 27 packages available in R to perform XAI analysis, (3) we present an example of an application of particular packages, (4) we acknowledge recent trends in XAI. The article is primarily devoted to the tools available in R, but since it is easy to integrate the Python code, we will also show examples for the most popular libraries from Python.
