Recent years have seen a substantial development of quantitative methods, mostly led by the computer science community with the goal of developing better machine learning applications, mainly focused on predictive modeling. However, economic, management, and technology forecasting research has so far been hesitant to apply predictive modeling techniques and workflows. In this paper, we introduce a machine learning (ML) approach to quantitative analysis geared towards optimizing the predictive performance, contrasting it with standard practices inferential statistics, which focus on producing good parameter estimates. We discuss the potential synergies between the two fields against the backdrop of this, at first glance, target-incompatibility. We discuss fundamental concepts in predictive modeling, such as out-of-sample model validation, variable and model selection, generalization, and hyperparameter tuning procedures. We are providing a hands-on predictive modeling introduction for a quantitative social science audience while aiming at demystifying computer science jargon. We use the illustrative example of patent quality estimation - which should be a familiar topic of interest in the Scientometrics community - guiding the reader through various model classes and procedures for data pre-processing, modeling, and validation. We start off with more familiar easy to interpret model classes (Logit and Elastic Nets), continues with less familiar non-parametric approaches (Classification Trees, Random Forest, Gradient Boosted Trees), and finally presents artificial neural network architectures, first a simple feed-forward and then a deep autoencoder geared towards rare-event prediction.
翻译:近些年来,主要由计算机科学界牵头的定量方法有了实质性的发展,其目标主要是开发更好的机器学习应用,主要侧重于预测模型;然而,经济、管理和技术预测研究迄今一直对应用预测模型技术和工作流程犹豫不决。在本论文中,我们采用了一种机器学习(ML)方法进行定量分析,目的是优化预测性能,与标准做法推断统计数据形成对比,重点是得出良好的参数估计。我们讨论了两个领域之间潜在的协同作用,目的是开发更好的机器学习应用,主要侧重于预测性模型;但我们讨论了预测性模型的基本概念,例如,在预测性模型校外校外校外校外校外校外校外校外校外校外校外校外校外校外院外校外校外院外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外院外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外校外)