Stability-Based Model Selection in Non-Stationary Environment

Stability-Based Model Selection in Non-Stationary Environment

Ivanov, Viktor (TU Delft Electrical Engineering, Mathematics and Computer Science)

Tax, David (mentor)

Delft University of Technology

2017-08-31

Model selection is associated to model assessment, which is the problem of comparing different models, or model hyperparameters, for a particular learning task. It constitutes a fundamental step in building machine learning models. The central question is: How a model will work in the future? In this thesis, a new model selection scheme for learning algorithms operating in non-stationary environment is introduced which is based on a notion of stability. In the evaluation part, the performance of the stability measure is studied for loss given default (LGD) model selection. The evaluation is based on a real-world LGD dataset provided by an anonymous financial institution.

machine learning
concept drift
model validation
credit risk
loss given default

http://resolver.tudelft.nl/uuid:31bbf0f5-c43a-4dc0-b2d5-59880368f2a3

Student theses

master thesis

© 2017 Viktor Ivanov