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Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features
Shapley values are today extensively used as a model-agnostic explanation framework to explain complex predictive machine learning …
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Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
Explaining complex or seemingly simple machine learning models is an important practical problem. We want to explain individual …
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Explaining predictive models using Shapley values and non-parametric vine copulas
In this paper the goal is to explain predictions from complex machine learning models. One method that has become very popular during …
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Detecting money laundering transactions with machine learning
Purpose The purpose of this paper is to develop, describe and validate a machine learning model for prioritising which financial …
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Estimating seal pup production in the Greenland Sea by using Bayesian hierarchical modelling
The Greenland Sea is an important breeding ground for harp and hooded seals. Estimates of annual seal pup production are critical …
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Investigating mesh-based approximation methods for the normalization constant in the log Gaussian Cox process likelihood
The log Gaussian Cox process (LGCP) is a frequently applied method for modeling point pattern data. The normalization constant of the …
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Pairwise local Fisher and Naı̈ve Bayes: Improving two standard discriminants
The Fisher discriminant is probably the best known likelihood discriminant for continuous data. Another benchmark discriminant is the …
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shapr: An R-package for explaining machine learning models with dependence-aware Shapley values
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What price semiparametric Cox regression?
Cox’s proportional hazards regression model is the standard method for modelling censored life-time data with covariates. In its …
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Parametric or nonparametric: The FIC approach
Should one rely on a parametric or nonparametric model when analysing a given data set? This classic question cannot be answered by …
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