6 papers
Beyond Additive Decompositions: Interpretability Through Separability
Jinyang Liu, Munir Eberhardt Hiabu
Interpretable machine learning requires models that are accurate and structurally faithful to the data. Existing explainability methods rely heavily on additive representations (e.…
A machine learning approach based on survival analysis for IBNR frequencies in non-life reserving
Munir Hiabu, Emil Hofman, Gabriele Pittarello
We introduce new approaches for forecasting IBNR (Incurred But Not Reported) frequencies by leveraging individual claims data, which includes accident date, reporting delay, and po…
Pure interaction effects unseen by Random Forests
Ricardo Blum, Munir Hiabu, Enno Mammen +1
Random Forests are widely claimed to capture interactions well. However, some simple examples suggest that they perform poorly in the presence of certain pure interactions that the…
Smooth Backfitting for Additive Hazard Rates
Stephan M. Bischofberger, Munir Hiabu, Enno Mammen +1
Smooth backfitting was first introduced in an additive regression setting via a direct projection alternative to the classic backfitting method by Buja, Hastie and Tibshirani. This…
Fast Estimation of Partial Dependence Functions using Trees
Jinyang Liu, Tessa Steensgaard, Marvin N. Wright +2
Many existing interpretation methods are based on Partial Dependence (PD) functions that, for a pre-trained machine learning model, capture how a subset of the features affects the…
Replicating and extending chain-ladder via an age-period-cohort structure on the claim development in a run-off triangle
Gabriele Pittarello, Munir Hiabu, Andrés M. Villegas
This paper introduces yet another stochastic model replicating chain-ladder estimates and furthermore considers extensions that add flexibility to the modeling. In its simplest for…