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As the actuarial landscape becomes increasingly data-driven,
traditional statistical methods are evolving, linking point
estimates with quantifiable uncertainty. Conformal Prediction is a
powerful framework used to evaluate the uncertainty of predictions.
It turns point predictions into prediction regions, in this way,
when you make a prediction, the output has probabilistic guarantees
that it covers the true outcome. In this web session, we will
explore the theoretical foundations of Conformal Prediction, its
assumptions, methodology, and advantages over traditional
approaches. Participants will see how this technique can be a
disruptive innovation in risk assessment, pricing, reserving, and
forecasting by integrating uncertainty directly into predictions.
The session is balanced between theory and hands-on activities,
offering real-world examples and code demonstrations in
classification, regression, time series, natural language
processing (NLP), and computer vision, all applied to challenges in
the actuarial field.
This web session aims to equip actuarial professionals with the
knowledge and skills behind Conformal Prediction, improving the
reliability and interpretability of their predictive models. By the
end of the course, participants will understand how to apply
conformal methods to various types of data and leverage these
techniques to improve decision-making processes in actuarial
tasks.
Early-bird discount is available for bookings made by 1
May 2025.
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