论文标题
driveml：无人驾驶机器学习的R包装
DriveML: An R Package for Driverless Machine Learning
论文作者
论文摘要
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）主要是指用于模型选择的自动化方法和各种算法的超参数优化，例如随机森林，梯度增强，神经网络等。在本文中，我们引入了一个新的软件包，即用于自动机器学习的驱动器。 Driveml通过运行该功能而不是编写冗长的R代码来帮助实现自动化机器学习管道的某些支柱，例如自动数据准备，功能工程，模型构建和模型解释。 Driveml包装可在Cran中找到。我们将driveml软件包与cran/github中的其他相关软件包进行了比较，并发现driveml在不同参数上表现最好。我们还通过在现实世界数据集上应用带有默认配置的driveml软件包来提供插图。总体而言，Driveml的主要好处是节省开发时间，减少开发人员的错误，对机器学习模型的最佳调整以及可重复性。
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.
