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In recent years, machine learning techniques have found their
way into the insurance world. While these methods generally improve
model accuracy, both explainability and manual interventions
continue to play a key role in risk and tariff modelling. This is
why practitioners in many lines of business still apply Generalised
Linear Models (GLMs) today for non-life pricing.
But conventional modelling with GLMs comes with downsides. It is a
mostly manual and step-by-step process, which may result in
overfitting or unrecognised main/interaction effects.
However, GLMs do offer variants in the flavour of machine learning
that automatically adapt to patterns in the data. These techniques
are known as regularised GLMs, and their most prominent versions
are the Lasso, Ridge regression and elastic nets. Not only can
these methods proactively prevent overfitting but also adaptively
learn non-linear patterns in the data along with an implicitly
integrated pre-processing and selection of variables.
During the web session, we will first explore the theoretical
foundations of regularised GLMs and the explicit design of the
algorithm. The remainder will then be hands-on as we provide
extensive code that implements the algorithm in the statistical
programming language R. We will discuss and run the code. You will
learn how to use the programme and apply the algorithm to non-life
claims data for pricing. Further focus will be on the visualization
of the results, especially on the insights gained from the learned
meta-results of the algorithm, e.g., the implicit way how we
selected, prioritised and pre-processed variables.
Your early-bird registration fee is € 300.00 plus 19% VAT for
bookings by
2 October 2023. After this date, the fee will be € 400.00 plus 19%
VAT. |
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