While machine learning models have become a mainstay in Cheminformatics, the field has yet to agree on standards for model evaluation and comparison. In many cases, authors compare methods by performing multiple folds of cross-validation and reporting the mean value for an evaluation metric such as the area under the receiver operating characteristic. These comparisons of mean values often lack statistical rigor and can lead to inaccurate conclusions. In the interest of encouraging best practices, this tutorial provides an example of how multiple methods can be compared in a statistically rigorous fashion.

中文翻译：

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比较分类模型——实用教程

虽然机器学习模型已成为化学信息学的支柱，但该领域尚未就模型评估和比较的标准达成一致。在许多情况下，作者通过执行多重交叉验证并报告评估指标的平均值来比较方法，例如接收器操作特征下的区域。这些平均值的比较通常缺乏统计严谨性，并可能导致不准确的结论。为了鼓励最佳实践，本教程提供了一个示例，说明如何以统计严格的方式比较多种方法。