Deep learning models are being adopted and applied on various critical decision-making tasks, yet they are trained to provide point predictions without providing degrees of confidence. The trustworthiness of deep learning models can be increased if paired with uncertainty estimations. Conformal Prediction has emerged as a promising method to pair machine learning models with prediction intervals, allowing for a view of the model's uncertainty. However, popular uncertainty estimation methods for conformal prediction fail to provide heteroskedastic intervals that are equally accurate for all samples. In this paper, we propose a method to estimate the uncertainty of each sample by calculating the variance obtained from a Deep Regression Forest. We show that the deep regression forest variance improves the efficiency and coverage of normalized inductive conformal prediction on a drug response prediction task.

翻译：深度学习模型正被应用于各种关键决策任务，但通常仅提供点预测而无置信度评估。通过结合不确定性估计，可提升深度学习模型的可信度。共形预测已成为一种有前景的方法，可赋予机器学习模型预测区间，从而展示模型的不确定性。然而，当前流行的共形预测不确定性估计方法难以提供对所有样本均具有同等准确性的异方差区间。本文提出一种方法，通过计算深度回归森林的方差来估计每个样本的不确定性。实验表明，在药物响应预测任务中，深度回归森林的方差有效提升了归一化归纳共形预测的效率与覆盖范围。