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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 this web session, we will first explore the theoretical
foundations of both the bias-variance trade-off in predictive
modelling and general GLM regularisation. We will then study the
explicit design of the algorithm. The remainder will 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 using a realistic case study in actuarial claims frequency
modelling. 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 visualisation 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 € 540.00 (net) / € 642.60
(incl. VAT, if applicable) for bookings by 3 October 2024. After
this date, the fee will be € 700.00 (net) / € 833.00 (incl. VAT, if
applicable). |
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