When Interpretability Matters: Shrinkage Regression
November 18, 2019
Estrelsaal C1 & C2
The acceptance of machine learning algorithms in predictive analytics heavily depends on the interpretability of the results. Standard regression techniques provide superior interpretability and allow for straightforward incorporation of expert knowledge but are often outperformed by black box algorithms in terms of predictive power. This session introduces shrinkage regression that overcomes shortcomings of standard regression (overfitting, moderate predictive performance, computationally intensive variable selection procedures) and allows the usage of very wide datasets. Steffen will demonstrate the advantages and elegance of shrinkage regression for causal and(!) predictive analytics and demonstrate why it belongs into the tool box of every data scientist.