Clemens Stachl & Florian Pargent
Termin: Sonntag, 16.09.2018, 9-17h
Ort: SH 0.106 (Seminarhaus)
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The majority of psychological research is aimed at explaining human experience and behavior with methods of inferential statistics. This is not always in line with the intention to predict psychological variables and associated outcomes with utmost precision (Yarkoni, 2017). Models and techniques from the field of machine learning were developed to achieve a maximum of predictive performance. Whereas, machine learning models have long been considered black-boxes, recent developments have greatly increased their interpretability. For these reasons, the psychological research community shows increasing interest in adopting these methods. In this workshop, we will give a non-technical introduction to the basic concepts and ideas of machine learning. We will discuss the bias variance tradeoff, overfitting, resampling techniques, model evaluation and variable selection. Participants will be introduced to the Random Forest (Breiman, 2001), a powerful, nonlinear machine learning algorithm that is known for its high predictive performance in many application settings. To demonstrate the strengths of the Random Forest, we will compare its performance with linear regression models in a series of benchmark experiments. In addition to performance evaluation, researchers are often interested in the importance of single predictors. In this regard, variable importance measures and partial dependency plots are useful. After this workshop, participants should be able to apply basic machine learning techniques to their own research.
Basic knowledge of R: load data files; perform statistical analyses; linear regression