Due to the intricacies surrounding taste profiles, one's view of good wine is subjective. Therefore, it is advantageous to provide a more objective, data‐driven way to assess wine preferences. Motivated by a previous study that modeled wine preferences using machine learning algorithms, this work presents an ensemble approach to predict a wine sample's quality level given its physiochemical properties. Results show the proposed framework out‐performs many sophisticated models including the one recommended by the motivational study. Moreover, the proposed framework offers a simple variable importance strategy to gain insight as to the relevance of the predictor variables and is applied to both simulated and real data. Given the predictive power of using ensembles, especially when they can be interpretable, practitioners can use the following approach to provide an accurate and inferential perspective towards demystifying wine preferences.

中文翻译：

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葡萄酒的分析吐司：使用堆叠概括来预测葡萄酒的偏爱

由于围绕口味的复杂性，人们对优质葡萄酒的看法是主观的。因此，提供一种更客观，以数据为依据的方法来评估葡萄酒的偏爱是有利的。之前的一项研究使用机器学习算法对葡萄酒的喜好进行建模，因此这项工作提出了一种综合方法，根据其理化性质来预测葡萄酒样品的质量水平。结果表明，提出的框架优于许多复杂的模型，包括动机研究推荐的模型。此外，所提出的框架提供了一种简单的变量重要性策略，以获取关于预测变量的相关性的见解，并被应用于模拟数据和真实数据。鉴于使用合奏的预测能力，尤其是在可以解释时，