slds-lmu/iml-shiny-summary

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R
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MIT License
Created over 5 years ago
Updated about 5 years ago

slds-lmu/iml-shiny-summary

slds-lmu

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Shiny Dashboard showing an interpretation summary for any model

seminar_multimodal_dl

https://slds-lmu.github.io/seminar_multimodal_dl/

lecture_i2ml

I2ML lecture repository

iml_methods_limitations

Seminar on Limitations of Interpretable Machine Learning Methods

yahpo_gym

Surrogate benchmarks for HPO problems

Jupyter Notebook

imlplots

Create Interpretable Machine Learning plots with an interactive Shiny based dashboard

lecture_dl4nlp

Repo containing all the lecture material for the dl4nlp course

paper_2019_iml_measures

Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition

lecture_iml

Jupyter Notebook

code_pitfalls_iml

This repository contains the code for all figures in the paper "General Pitfalls of Model-agnostic Interpretation Methods for Machine Learning Models"

i2ml

https://slds-lmu.github.io/i2ml/

seminar_nlp_ss20

Link to website

lecture_optimization

tsclassification

Wrapping the Time-Series Classification Java Implementations for R

mosmafs

Multi-Objective Simultaneous Model and Feature Selection

latex-math

qdo_yahpo

A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models

lecture_i2ml_learnr_tutorials

lrz_configs

yahpo_data

Data required for slds-lmu/yahpo_gym

paper_2021_categorical_feature_encodings

hpo_ela

paper_2023_survival_benchmark

Benchmark for Burk et al. (2024)

i2dl

https://slds-lmu.github.io/i2dl/

wildlife-ml

paper_2021_xautoml

Jupyter Notebook

lecture_sl

vistool

wildlife-experiments

Jupyter Notebook

seminar_website_skeleton

jTSC4R

Java Time Series Classification code to use in R

paper_2021_multi_fidelity_surrogates

Surrogate benchmarks for HPO problems

surrogates

grouped_feat_imp_and_effects

dl4nlp

https://slds-lmu.github.io/dl4nlp/

rcourses_notebook_deeplearning

Jupyter Notebook

paper_2019_multiobjective_rfms

High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions

Jupyter Notebook

lecture_advml

yahpo_exps

Experiments for yahpo gym

lecture_template

rcourses_notebook_clustering

Jupyter Notebook

paper_2019_variationalResampleDistributionShift

Variational Resampling Based Assessment of Deep Neural Networks Robustness under Distribution Shift

mlw-htr

Jupyter Notebook

ame

average marginal effects for machine learning

qdo_nas

paper_2024_rpid

benchmark_2022_counterfactuals

Benchmark code for paper on counterfactuals R Package

phd_thesis_dummy_template

rcourses_notebook_ml

Jupyter Notebook

mbt_comparison

mobafeas

Model Based Feature Selection

introduction_iml_bliz_summerschool

This repository provides a short introduction into the most popular model-agnostic IML (interpretable machine learning) methods. A presentation containing the theoretical background and examples as well as excercises on real-world data are included.

lecture_i2dl

Introduction to Deep Leaning

Jupyter Notebook

lecture_service

Service repo for common infrastructure across all open source lectures

paper_2023_eagga

Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models

paper_2023_regression_suite