bips-hb/iml_exercise

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bips-hb/iml_exercise

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Interpretable Machine Learning Lecture WS2021 Uni Bremen: Exercise Sheets

innsight

Interpretability methods to analyze the behavior and individual predictions of modern neural networks in R.

neuralnet

Training of Neural Networks

arfpy

Python implementation of adversarial random forests for density estimation and generative modelling

Jupyter Notebook

pvm

A collection of signal detection methods used in the field of pharmacovigilance

cpi

CPI: Conditional Predictive Impact

APTS_Causal_Inference

Practicals for the APTS Module "Causal Inference"

blockForest

Random Forests for Blocks of Clinical and Omics Covariate Data

arf

Adversarial Random Forests

methods_week

ZeSOB Methods Weeks in Statistics on Interpretable Machine Learning

srsim

An R package for simulating spontaneous reports

pvmcomparison

R package for comparing various methods used in the field of pharmacovigilance to detect associations between drugs and adverse events in spontaneous reporting data

DataTrainCausalLearning

Practicals for the Data Train Course "Causal learning" 2021 (V Didelez)

datatrain_workshop_ml

Workshop / hands-on component to the Data Train ML workshop

iml_bremen_2023

Exercises for the Lecture in Interpretable Machine Learning taught at the University of Bremen in 2023

Jupyter Notebook

generative_rf

Generative Random Forests

survnet

Artificial neural networks for survival analysis

arf_paper

Code and materials to reproduce adversarial RF paper

micd

Multiple Imputation in Causal Graph Discovery

rgp

Identification of Risk Groups in Pharmacovigilance Using Penalized Regression (RGP)

tpc

tPC - Causal discovery with temporal background

CFI_mixedData

Code for paper "Conditional Variable Importance for Mixed Data" by Kristin Blesch, David S. Watson, Marvin N. Wright (2022)

countARFactuals

This repository contains the code for the countARFactuals paper.