Automated machine learning (AutoML) streamlines the creation of ML models. While most methods select the "best" model based on predictive quality, it's crucial to acknowledge other aspects, such as interpretability and resource consumption. This holds particular importance in the context of deep neural networks (DNNs), as these models are often perceived as computationally intensive black boxes. In the challenging domain of time series forecasting, DNNs achieve stunning results, but specialized approaches for automatically selecting models are scarce. In this paper, we propose AutoXPCR - a novel method for automated and explainable multi-objective model selection. Our approach leverages meta-learning to estimate any model's performance along PCR criteria, which encompass (P)redictive error, (C)omplexity, and (R)esource demand. Explainability is addressed on multiple levels, as our interactive framework can prioritize less complex models and provide by-product explanations of recommendations. We demonstrate practical feasibility by deploying AutoXPCR on over 1000 configurations across 114 data sets from various domains. Our method clearly outperforms other model selection approaches - on average, it only requires 20% of computation costs for recommending models with 90% of the best-possible quality.

翻译：自动化机器学习（AutoML）简化了机器学习模型的创建过程。尽管多数方法基于预测质量选择“最佳”模型，但其他方面——如可解释性和资源消耗——同样至关重要。在深度神经网络（DNN）领域，这一点尤为突出，因为这些模型常被视为计算密集型的黑箱模型。在具有挑战性的时间序列预测领域，DNN取得了惊人成果，但自动选择模型的专用方法却十分匮乏。本文提出AutoXPCR——一种用于自动化且可解释的多目标模型选择的新方法。该方法利用元学习，通过PCR标准（包括预测误差（P）、复杂度（C）和资源需求（R））评估任意模型的性能。可解释性在多个层面得以体现：我们的交互式框架可优先选择复杂度较低的模型，并提供推荐结果的附加解释。通过将AutoXPCR部署到涵盖不同领域的114个数据集的1000余种配置上，我们验证了其实用可行性。我们的方法明显优于其他模型选择方法——平均而言，在推荐具有最佳质量90%的模型时，仅需20%的计算成本。