On Enhancing the Interpretability of Data Science Models via Dimensionality Reduction

Dolores Romero Morales (Copenhagen Business School)

16-Jun-2020, 18:30-19:00 (4 years ago)

Abstract: Data Science aims to develop models that extract knowledge from complex data to aid Data Driven Decision Making. There is a growing literature on enhancing the interpretability of Data Science methods. Interpretability is desirable for non-experts; it is required by regulators for models aiding, for instance, credit scoring; and since 2018 the EU has extended this requirement by imposing the so-called right-to-explanation. Mathematical Optimization has shown a crucial role when striking a balance between interpretability and accuracy, having LASSO as one of the main exponents. In this presentation, we will navigate through some novel dimensionality reduction techniques embedded in the construction of data science models, to enhance their interpretability.

optimization and control

Audience: researchers in the topic


Discrete Optimization Talks

Series comments: DOTs are virtual discrete optimization talks, organized by Aleksandr M. Kazachkov and Elias B. Khalil. To receive updates about upcoming DOTs, please join our mailing list. Topics of interest include theoretical, computational, and applied aspects of integer and combinatorial optimization.

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Organizers: Discrete Optimization Talks*, Aleksandr M. Kazachkov*, Elias B. Khalil
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