论文标题
SVM可行性和可解释性的加权解决方案
A Weighted Solution to SVM Actionability and Interpretability
论文作者
论文摘要
机器学习中的研究已成功开发出算法以构建准确的分类模型。但是，在许多现实世界中的应用程序（例如医疗保健，客户满意度和环境保护）中，我们希望能够使用模型来决定采取什么行动。 我们在支持向量机的背景下研究了可行性的概念。可行性与机器学习模型的解释性或解释性一样重要，这是一个持续而重要的研究主题。可行性是使我们能够对机器学习模型及其预测采取行动的方法。 本文为线性和非线性SVM模型上的可操作问题找到了解决方案。此外，我们介绍了一种考虑加权动作的方法，该操作允许某些功能的变化比其他功能更大。我们在线性，RBF和多项式内核上提出了梯度下降溶液，并测试了模型对合成数据集和真实数据集的有效性。我们还能够通过可操作的角度探索模型的可解释性。
Research in machine learning has successfully developed algorithms to build accurate classification models. However, in many real-world applications, such as healthcare, customer satisfaction, and environment protection, we want to be able to use the models to decide what actions to take. We investigate the concept of actionability in the context of Support Vector Machines. Actionability is as important as interpretability or explainability of machine learning models, an ongoing and important research topic. Actionability is the task that gives us ways to act upon machine learning models and their predictions. This paper finds a solution to the question of actionability on both linear and non-linear SVM models. Additionally, we introduce a way to account for weighted actions that allow for more change in certain features than others. We propose a gradient descent solution on the linear, RBF, and polynomial kernels, and we test the effectiveness of our models on both synthetic and real datasets. We are also able to explore the model's interpretability through the lens of actionability.
