Predictions of nuclear properties far from measured data are inherently inaccurate because of uncertainties in our knowledge of nuclear forces and in our treatment of quantum many-body effects in strongly-interacting systems. While the model bias can be directly calculated when experimental data is available, only an estimate can be made in the absence of such measurements. Current approaches to compute the estimated bias quickly lose predictive power when their input variables are taken far from the training region, resulting in uncontrolled uncertainties in applications such as nucleosynthesis simulations. In this letter, we present a novel technique to identify the input variables of machine learning algorithms that can provide robust estimates of model bias. Our process is based on selecting input variables, or features, based on their probability distribution functions across the entire nuclear chart. We illustrate our approach on the problem of quantifying the model bias in nuclear binding energies calculated with Density Functional Theory (DFT). We prove that building model biases with only Z and N as features leads to highly unreliable extrapolations. Conversely, we show that proper feature selection can systematically improve theoretical predictions without increasing uncertainties.

由于我们对核力的认识以及我们对强相互作用系统中的量子多体效应的处理存在不确定性，因此远离测量数据对核特性的预测本质上是不准确的。虽然可以在有实验数据时直接计算模型偏差，但在没有此类测量的情况下只能进行估计。当输入变量远离训练区域时，当前计算估计偏差的方法会迅速失去预测能力，从而导致核合成模拟等应用中的不确定性不受控制。在这封信中，我们提出了一种新技术来识别机器学习算法的输入变量，它可以提供模型偏差的稳健估计。我们的过程基于选择输入变量或特征，基于它们在整个核图上的概率分布函数。我们说明了我们对用密度泛函理论 (DFT) 计算的核结合能模型偏差问题进行量化的方法。我们证明了构建模型的偏差只有Z和N作为特征会导致非常不可靠的推断。相反，我们表明适当的特征选择可以系统地改进理论预测而不会增加不确定性。