分类数据在数据科学项目中作为石头客人，用于预测有缺陷的水表
Categorical data as a stone guest in a data science project for predicting defective water meters
摘要：
经过一年的研究，我们开发了一个基于机器学习的分类器，用于预测机械水表是否会随着时间的推移和密集使用而失效。一个循环的深度神经网络（RNN）是用从100万米收集的1500万个用水量读数外推的数据训练的。我们用于训练的数据基本上有两种类型：连续和分类。分类的是一种数据类型，可以根据某些定性属性采取有限和固定数量的可能值之一;而连续的，在这种情况下，是测量值。以米为单位的用水量（立方米）。在本文中，我们想讨论的事实是，虽然我们的RNN的预测准确率平均超过了80%，但基于连续数据的使用，这些性能并没有随着训练阶段分类信息的引入而显著提高。从一个特定的角度来看，这仍然是我们研究的一个未解决的关键问题。然而，如果我们从数据科学的角度来分析这个有争议的案例，我们就会意识到，如果没有领域专家的参与，我们就无法构建准确的机器学习解决方案，他们可以区分不同类型数据的重要性（之间的关系），每种数据都有自己的意义，有效性和含义。经过所有最初的炒作，数据科学正朝着多方面的学科发展，数据科学家/机器学习专家和领域专家的设计是共生的
英文摘要：
After a one-year long effort of research on the field, we developed a machine learning-based classifier, tailored to predict whether a mechanical water meter would fail with passage of time and intensive use as well. A recurrent deep neural network (RNN) was trained with data extrapolated from 15 million readings of water consumption, gathered from 1 million meters. The data we used for training were essentially of two types: continuous vs categorical. Categorical being a type of data that can take on one of a limited and fixed number of possible values, on the basis of some qualitative property; while continuous, in this case, are the values of the measurements. taken at the meters, of the quantity of consumed water (cubic meters). In this paper, we want to discuss the fact that while the prediction accuracy of our RNN has exceeded the 80% on average, based on the use of continuous data, those performances did not improve, significantly, with the introduction of categorical information during the training phase. From a specific viewpoint, this remains an unsolved and critical problem of our research. Yet, if we reason about this controversial case from a data science perspective, we realize that we have had a confirmation that accurate machine learning solutions cannot be built without the participation of domain experts, who can differentiate on the importance of (the relation between) different types of data, each with its own sense, validity, and implications. Past all the original hype, the science of data is thus evolving towards a multifaceted discipline, where the designitations of data scientist/machine learning expert and domain expert are symbiotic
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
