论文标题
机器学习与传统高级统计建模之间的相似性和差异
Similarities and Differences between Machine Learning and Traditional Advanced Statistical Modeling in Healthcare Analytics
论文作者
论文摘要
在确定最佳方法，机器学习或统计建模以解决分析挑战时，数据科学家和统计学家通常会偶然。但是，机器学习和统计建模比分析战场的不同方面的对手更多。选择两种方法或在某些情况下使用两者之间的选择是基于要解决的问题以及所需的结果以及可用于使用和分析情况的数据。基于类似的数学原理，机器学习和统计建模是互补的，但仅在整体分析知识库中使用不同的工具。确定主要的方法应基于要解决的问题以及经验证据，例如数据的大小和完整性，变量的数量，假设或缺乏，以及预期的结果，例如预测或因果关系。良好的分析师和数据科学家应精通技术及其适当的应用，从而使用正确的工具适合正确的项目来实现所需的结果。
Data scientists and statisticians are often at odds when determining the best approach, machine learning or statistical modeling, to solve an analytics challenge. However, machine learning and statistical modeling are more cousins than adversaries on different sides of an analysis battleground. Choosing between the two approaches or in some cases using both is based on the problem to be solved and outcomes required as well as the data available for use and circumstances of the analysis. Machine learning and statistical modeling are complementary, based on similar mathematical principles, but simply using different tools in an overall analytics knowledge base. Determining the predominant approach should be based on the problem to be solved as well as empirical evidence, such as size and completeness of the data, number of variables, assumptions or lack thereof, and expected outcomes such as predictions or causality. Good analysts and data scientists should be well versed in both techniques and their proper application, thereby using the right tool for the right project to achieve the desired results.
