DriveML: An R Package for Driverless Machine Learning
This provides a tool for R users to save development time and reduce errors in machine learning tasks, though it is incremental as it builds on existing AutoML concepts.
The authors tackled the problem of automating machine learning pipelines by introducing DriveML, an R package that performs automated data preparation, feature engineering, model building, and explanation, and found that it performs best compared to other packages across different parameters.
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.