In the construction of activity prediction models, the use of feature ranking methods is a useful mechanism for extracting information for ranking features in terms of their significance to develop predictive models. This paper studies the influence of feature rankers in the construction of molecular activity prediction models; for this purpose, a comparative study of fourteen rankings methods for feature selection was conducted. The activity prediction models were constructed using four well-known classifiers and a wide collection of datasets. The ranking algorithms were compared considering the performance of these classifiers using different metrics and the consistency of the ranked features.

中文翻译：

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特征等级对分子活性预测模型构建的影响。

在活动预测模型的构建中，特征排序方法的使用是一种有用的机制，可以从信息中提取特征的等级，以开发预测模型。研究了特征秩对分子活性预测模型构建的影响。为此目的，进行了十四种用于特征选择的排名方法的比较研究。活动预测模型是使用四个众所周知的分类器和大量数据集构建的。考虑到使用不同指标的这些分类器的性能以及排名特征的一致性，对排名算法进行了比较。