  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.
