Introduction to Predictive Modeling in Entrepreneurship and Innovation Studies: A Hands-On Application in the Prediction of Breakthrough Patents

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Abstract

Recent years have seen a substantial development of quantitative methods, mostly led by the computer science community with the goal to develop better machine learning application, mainly focused on predictive modeling. However, the field of innovation and entrepreneurship research has up to now been hesitant to apply predictive modeling techniques and workflows. In this paper, we introduce to a machine learning (ML) approach to quantitative analysis geared towards optimizing the predictive performance, contrasting it with standard practices in econometrics which focus on producing good parameter estimates. We discuss the potential synergies between the two fields against the backdrop of this at first glans \enquote{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. Providing a hands-on predictive modelling for an quantitative social science audience, while aiming at demystifying computer science jargon. We use the example of \enquote{high-quality} patent identification guiding the reader through various model classes and procedures for data pre-processing, modelling and validation. We start of with more familiar easy to interpret model classes (Logit and Elastic Nets), continues with less familiar nonparametric approaches (Classification Trees and Random Forest) and finally presents deep autoencoder based anomaly detection. Instead of limiting ourselves to the introduction of standard ML techniques, we also present state-of-the-art yet approachable techniques from artificial neural networks and deep learning to predict rare phenomena of interest.
Original languageEnglish
Publication statusUnpublished - 2018

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Innovation
Learning systems
Computer science
Social sciences
Identification (control systems)
Tuning
Neural networks
Processing
Chemical analysis

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abstract = "Recent years have seen a substantial development of quantitative methods, mostly led by the computer science community with the goal to develop better machine learning application, mainly focused on predictive modeling. However, the field of innovation and entrepreneurship research has up to now been hesitant to apply predictive modeling techniques and workflows. In this paper, we introduce to a machine learning (ML) approach to quantitative analysis geared towards optimizing the predictive performance, contrasting it with standard practices in econometrics which focus on producing good parameter estimates. We discuss the potential synergies between the two fields against the backdrop of this at first glans \enquote{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. Providing a hands-on predictive modelling for an quantitative social science audience, while aiming at demystifying computer science jargon. We use the example of \enquote{high-quality} patent identification guiding the reader through various model classes and procedures for data pre-processing, modelling and validation. We start of with more familiar easy to interpret model classes (Logit and Elastic Nets), continues with less familiar nonparametric approaches (Classification Trees and Random Forest) and finally presents deep autoencoder based anomaly detection. Instead of limiting ourselves to the introduction of standard ML techniques, we also present state-of-the-art yet approachable techniques from artificial neural networks and deep learning to predict rare phenomena of interest.",
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