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Recent years have seen a substantial development of quantitative methods,mostly led by the computer science community with the goal of developing bettermachine learning applications, mainly focused on predictive modeling. However,economic, management, and technology forecasting research has so far beenhesitant to apply predictive modeling techniques and workflows. In this paper,we introduce a machine learning (ML) approach to quantitative analysis gearedtowards optimizing the predictive performance, contrasting it with standardpractices inferential statistics, which focus on producing good parameterestimates. We discuss the potential synergies between the two fields againstthe backdrop of this, at first glance, target-incompatibility. We discussfundamental concepts in predictive modeling, such as out-of-sample modelvalidation, variable and model selection, generalization, and hyperparametertuning procedures. We are providing a hands-on predictive modeling introductionfor a quantitative social science audience while aiming at demystifyingcomputer science jargon. We use the illustrative example of patent qualityestimation - which should be a familiar topic of interest in the Scientometricscommunity - guiding the reader through various model classes and procedures fordata pre-processing, modeling, and validation. We start off with more familiareasy to interpret model classes (Logit and Elastic Nets), continues with lessfamiliar non-parametric approaches (Classification Trees, Random Forest,Gradient Boosted Trees), and finally presents artificial neural networkarchitectures, first a simple feed-forward and then a deep autoencoder gearedtowards rare-event prediction.

Introduction to Rare-Event Predictive Modeling for Inferential Statisticians -- A Hands-On Application in the Prediction of Breakthrough Patents