R语言专用自动化机器学习工具，轻松训练树模型。
forester: A Tree-Based AutoML Tool in R
- 基于树结构的AutoML方法，自动完成数据预处理与建模
- 支持分类、回归及部分生存分析任务，操作简单高效
- 适合不熟悉机器学习的R用户快速上手
大多数自动化机器学习（AutoML）工具基于Python开发，但许多数据科学家仍使用R语言。然而现有的R解决方案有限，且门槛较高，需要具备机器学习知识。为填补这一空白，我们提出forester包，这是一个开源的R语言AutoML工具，专为表格数据上的高质量树模型训练而设计。它全面支持二分类和多分类任务、回归任务，部分支持生存分析。用户仅需少数函数即可完成数据质量检测、预处理流程构建、树模型训练与调优、结果评估，并生成用于进一步分析的报告。
原文摘要 · Abstract (English)
The majority of automated machine learning (AutoML) solutions are developed in Python, however a large percentage of data scientists are associated with the R language. Unfortunately, there are limited R solutions available. Moreover high entry level means they are not accessible to everyone, due to required knowledge about machine learning (ML). To fill this gap, we present the forester package, which offers ease of use regardless of the user's proficiency in the area of machine learning. The forester is an open-source AutoML package implemented in R designed for training high-quality tree-based models on tabular data. It fully supports binary and multiclass classification, regression, and partially survival analysis tasks. With just a few functions, the user is capable of detecting issues regarding the data quality, preparing the preprocessing pipeline, training and tuning tree-based models, evaluating the results, and creating the report for further analysis.
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