• Stars
    star
    2
  • Language
    Jupyter Notebook
  • Created about 1 year ago
  • Updated 11 months ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

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

More Repositories

1

innsight

Interpretability methods to analyze the behavior and individual predictions of modern neural networks in R.
R
26
star
2

neuralnet

Training of Neural Networks
R
25
star
3

arfpy

Python implementation of adversarial random forests for density estimation and generative modelling
Jupyter Notebook
23
star
4

pvm

A collection of signal detection methods used in the field of pharmacovigilance
R
18
star
5

cpi

CPI: Conditional Predictive Impact
R
11
star
6

APTS_Causal_Inference

Practicals for the APTS Module "Causal Inference"
HTML
9
star
7

blockForest

Random Forests for Blocks of Clinical and Omics Covariate Data
C++
7
star
8

arf

Adversarial Random Forests
R
7
star
9

methods_week

ZeSOB Methods Weeks in Statistics on Interpretable Machine Learning
HTML
5
star
10

srsim

An R package for simulating spontaneous reports
R
4
star
11

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
R
4
star
12

DataTrainCausalLearning

Practicals for the Data Train Course "Causal learning" 2021 (V Didelez)
R
4
star
13

datatrain_workshop_ml

Workshop / hands-on component to the Data Train ML workshop
R
3
star
14

generative_rf

Generative Random Forests
R
2
star
15

survnet

Artificial neural networks for survival analysis
R
2
star
16

arf_paper

Code and materials to reproduce adversarial RF paper
Python
2
star
17

micd

Multiple Imputation in Causal Graph Discovery
R
2
star
18

rgp

Identification of Risk Groups in Pharmacovigilance Using Penalized Regression (RGP)
R
1
star
19

tpc

tPC - Causal discovery with temporal background
R
1
star
20

CFI_mixedData

Code for paper "Conditional Variable Importance for Mixed Data" by Kristin Blesch, David S. Watson, Marvin N. Wright (2022)
R
1
star
21

iml_exercise

Interpretable Machine Learning Lecture WS2021 Uni Bremen: Exercise Sheets
Jupyter Notebook
1
star
22

countARFactuals

This repository contains the code for the countARFactuals paper.
R
1
star