DriveML：一个用于无人驾驶机器学习的R包
DriveML: An R Package for Driverless Machine Learning
摘要：
近年来，自动化机器学习的概念变得非常流行。自动化机器学习（AutoML）主要是指随机森林、梯度提升、神经网络等算法的模型选择和超参数优化的自动化方法。本文介绍了一种新的自动化机器学习软件包DriveML。DriveML有助于实现自动化机器学习管道的一些支柱，例如自动化数据准备，功能工程，模型构建和模型解释，通过运行函数而不是编写冗长的R代码。DriveML包在CRAN中可用。我们将DriveML包与CRAN/Github中的其他相关包进行了比较，发现DriveML在不同参数下表现最好。我们还通过在真实世界的数据集上应用具有默认配置的DriveML包来提供说明。总体而言，DriveML的主要优势在于节省开发时间，减少开发人员的错误，优化机器学习模型和可重复性。
英文摘要：
In recent years, the concept of automated machine learning has become very popular. Automated Machine Learning (AutoML) mainly refers to the automated methods for model selection and hyper-parameter optimization of various algorithms such as random forests, gradient boosting, neural networks, etc. In this paper, we introduce a new package i.e. DriveML for automated machine learning. DriveML helps in implementing some of the pillars of an automated machine learning pipeline such as automated data preparation, feature engineering, model building and model explanation by running the function instead of writing lengthy R codes. The DriveML package is available in CRAN. We compare the DriveML package with other relevant packages in CRAN/Github and find that DriveML performs the best across different parameters. We also provide an illustration by applying the DriveML package with default configuration on a real world dataset. Overall, the main benefits of DriveML are in development time savings, reduce developer's errors, optimal tuning of machine learning models and reproducibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
