Contemporary QSAR Classifiers Compared

We present a comparative assessment of several state-of-the-art machine learning tools for mining drug data, including support vector machines (SVMs) and the ensemble decision tree methods boosting, bagging, and random forest, using eight data sets and two sets of descriptors. We demonstrate, by rigorous multiple comparison statistical tests, that these techniques can provide consistent improvements in predictive performance over single decision trees. However, within these methods, there is no clearly best-performing algorithm. This motivates a more in-depth investigation into the properties of random forests. We identify a set of parameters for the random forest that provide optimal performance across all the studied data sets. Additionally, the tree ensemble structure of the forest may provide an interpretable model, a considerable advantage over SVMs. We test this possibility and compare it with standard decision tree models.本研究针对8个数据集与两组描述符，对多款用于药物数据挖掘的前沿机器学习工具开展对比评估，其中涵盖支持向量机（support vector machines，SVM）以及提升（boosting）、装袋（bagging）、随机森林（random forest）等集成决策树方法。本研究通过严谨的多重比较统计检验证实，相较于单棵决策树，上述机器学习技术可在预测性能上取得稳定提升。然而在这些方法中，尚未出现性能显著最优的算法。这一发现促使我们针对随机森林的特性展开更为深入的研究，并确定了一组可使随机森林在全部受试数据集上均达到最优性能的参数。此外，随机森林的树集成结构可生成可解释模型，相较支持向量机（SVM）具备显著优势。本研究针对这一可能性开展验证，并将其与标准决策树模型进行对比。