NeuralNetTools: Visualization and Analysis Tools for Neural Networks
NeuralNetTools: Visualization and Analysis Tools for Neural Networks
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DOI：
10.18637/jss.v085.i11
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发表时间：
2018-07-01
影响因子：
5.8
通讯作者：
Beck, Marcus W.
中科院分区：
文献类型：
--
作者：
Beck, Marcus W.
监督神经网络已被用作机器学习技术来识别和预测多个变量之间的涌现模式。对这些方法的常见批评是无法描述拟合模型中变量之间的关系。尽管已经提出了几种“照亮黑匣子”的技术，但它们尚未在开源编程环境中提供。本文介绍了 NeuralNetTools 包，该包可用于解释在 R 中创建的监督神经网络模型。包中的函数可用于使用神经网络解释图可视化模型，通过分解模型权重来评估变量重要性，并对响应变量对输入变量变化进行敏感性分析。为 R 中许多常见神经网络包中的对象提供了方法，包括 caret、neuralnet、nnet 和 RSNNS。本文简要概述了神经网络的理论基础，描述了包结构和功能，并提供了一个应用示例，为使用 NeuralNetTools 进行模型开发提供了背景。总的来说，该软件包提供了一个神经网络工具集，补充了现有的数据密集型探索定量技术。
Supervised neural networks have been applied as a machine learning technique to identify and predict emergent patterns among multiple variables. A common criticism of these methods is the inability to characterize relationships among variables from a fitted model. Although several techniques have been proposed to "illuminate the black box", they have not been made available in an open-source programming environment. This article describes the NeuralNetTools package that can be used for the interpretation of supervised neural network models created in R. Functions in the package can be used to visualize a model using a neural network interpretation diagram, evaluate variable importance by disaggregating the model weights, and perform a sensitivity analysis of the response variables to changes in the input variables. Methods are provided for objects from many of the common neural network packages in R, including caret, neuralnet, nnet, and RSNNS. The article provides a brief overview of the theoretical foundation of neural networks, a description of the package structure and functions, and an applied example to provide a context for model development with NeuralNetTools. Overall, the package provides a toolset for neural networks that complements existing quantitative techniques for data-intensive exploration.