slds-lmu / 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"
☆13Updated 3 years ago
Related projects ⓘ
Alternatives and complementary repositories for code_pitfalls_iml
- Explaining dimensionality results using SHAP values☆52Updated 2 years ago
- Perform inference on algorithm-agnostic variable importance in Python☆20Updated 2 years ago
- ☆16Updated 2 years ago
- Reading history for Fair ML Reading Group in Melbourne☆37Updated 3 years ago
- Small Dataset Benchmarks on the Dataset of Datasets UCI++☆85Updated 2 years ago
- (ICML 2021) Mandoline: Model Evaluation under Distribution Shift☆31Updated 3 years ago
- This Python package implements algorithms for multiviews (multimodals) learning☆14Updated last month
- ☆54Updated 2 months ago
- Here we will post recitation materials.☆9Updated 4 years ago
- Contains public materials for students enrolled in MITx: 6.871x, Machine Learning for Healthcare☆20Updated 3 years ago
- This is the code for the paper Jacobian-based Causal Discovery with Nonlinear ICA, demonstrating how identifiable representations (partic…☆16Updated 2 months ago
- A lightweight implementation of removal-based explanations for ML models.☆57Updated 3 years ago
- A Python package for unwrapping ReLU DNNs☆70Updated 10 months ago
- Python library for Ceteris Paribus Plots (What-if plots)☆19Updated 3 years ago
- stand alone Neural Additive Models, forked from google-reasearch for easy import to colab☆27Updated 4 years ago
- XAI Stories. Case studies for eXplainable Artificial Intelligence☆29Updated 4 years ago
- Preprint/draft article/blog on some explainable machine learning misconceptions. WIP!☆28Updated 5 years ago
- Website for ML4H Symposium☆22Updated 5 months ago
- Functional matrix factorization via Bayesian tensor filtering☆13Updated last year
- A comparison of the dimensionality reduction results using t-SNE, UMAP, PCA, and TriMap☆30Updated 2 years ago
- A game theoretic approach to explain the output of any machine learning model.☆14Updated 2 years ago
- A repository containing the materials required to complete the "AAAI Lab for Innovative Uses of Synthetic Data". This includes tutorials …☆12Updated 2 months ago
- Uncertainty in Conditional Average Treatment Effect Estimation☆28Updated 3 years ago
- Workshop on Deep Learning for Health and Life Sciences☆14Updated last year
- Shows how to create basic image adversaries, and train adversarially robust image classifiers (to some extent).☆13Updated 4 years ago
- scikit-learn gradient-boosting-model interactions☆25Updated last year
- The Baseline Site Selection Tool implements simulation tools for clinical trial enrollment.☆18Updated 2 years ago
- ☆16Updated 3 years ago
- Adaptive and Reliable Classification: efficient conformity scores for multi-class classification problems☆31Updated last year
- A Python Package providing two algorithms, DAME and FLAME, for fast and interpretable treatment-control matches of categorical data☆57Updated 5 months ago