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.

本文介绍了一个名为 DriveML 的自动机器学习工具包，可用于自动数据准备，特征工程，模型构建和模型解释，并与其他相关工具包进行了比较。研究表明，DriveML 表现最佳，能够减少开发时间，降低开发人员的错误，优化机器学习模型并提高可重复性。