Automated machine learning (AutoML) and artificial neural networks (ANNs) have revolutionized the field of artificial intelligence by yielding incredibly high-performing models to solve a myriad of inductive learning tasks. In spite of their successes, little guidance exists on when to use one versus the other. Furthermore, relatively few tools exist that allow the integration of both AutoML and ANNs in the same analysis to yield results combining both of their strengths. Here, we present TPOT-NN—a new extension to the tree-based AutoML software TPOT—and use it to explore the behavior of automated machine learning augmented with neural network estimators (AutoML+NN), particularly when compared to non-NN AutoML in the context of simple binary classification on a number of public benchmark datasets. Our observations suggest that TPOT-NN is an effective tool that achieves greater classification accuracy than standard tree-based AutoML on some datasets, with no loss in accuracy on others. We also provide preliminary guidelines for performing AutoML+NN analyses, and recommend possible future directions for AutoML+NN methods research, especially in the context of TPOT.

自动化机器学习（AutoML）和人工神经网络（ANN）通过产生难以置信的高性能模型来解决众多归纳学习任务，彻底改变了人工智能领域。尽管它们取得了成功，但何时使用一种对另一种的指导却很少。此外，相对很少的工具允许在同一分析中将AutoML和ANN集成在一起，以产生结合了两者优势的结果。在这里，我们介绍TPOT-NN（这是对基于树的AutoML软件TPOT的新扩展），并使用它来探索由神经网络估计器（AutoML + NN）增强的自动化机器学习的行为，特别是与非NN AutoML相比在一些公共基准数据集上进行简单的二进制分类的情况下。我们的观察结果表明，TPOT-NN是一种有效的工具，在某些数据集上，其分类准确性高于基于标准树的AutoML，而在其他数据集上却没有损失准确性。我们还提供了执行AutoML + NN分析的初步指南，并建议了AutoML + NN方法研究的未来可能方向，尤其是在TPOT的情况下。