slds-lmu / code_pitfalls_imlLinks
This repository contains the code for all figures in the paper "General Pitfalls of Model-agnostic Interpretation Methods for Machine Learning Models"
☆15Updated 4 years ago
Alternatives and similar repositories for code_pitfalls_iml
Users that are interested in code_pitfalls_iml are comparing it to the libraries listed below
Sorting:
- Explaining dimensionality results using SHAP values☆55Updated last month
- ☆33Updated last year
- A lightweight implementation of removal-based explanations for ML models.☆59Updated 4 years ago
- Small Dataset Benchmarks on the Dataset of Datasets UCI++☆93Updated 3 years ago
- ☆56Updated last year
- Contains public materials for students enrolled in MITx: 6.871x, Machine Learning for Healthcare☆20Updated 4 years ago
- A spectral method for assessing and combining multiple data visualizations☆50Updated 2 years ago
- A Python Package providing two algorithms, DAME and FLAME, for fast and interpretable treatment-control matches of categorical data☆62Updated 6 months ago
- Quickest way to share everything about your research within a single app☆37Updated last year
- ESANN20 paper code repository. This package is a perplexity-free extension of Parametric t-SNE dimensionality reduction method implemente…☆23Updated 2 years ago
- Uniform Manifold Approximation with Two-phase Optimization (IEEE VIS 2022 short)☆114Updated 5 months ago
- Tools for training explainable models using attribution priors.☆125Updated 4 years ago
- A repo for transfer learning with deep tabular models☆105Updated 2 years ago
- Neural Additive Models (Google Research)☆74Updated 4 years ago
- A Natural Language Interface to Explainable Boosting Machines☆69Updated last year
- Pandas ExtensionDtypes for dealing with genomics data☆47Updated 8 months ago
- Code repository for the paper "Towards a Comprehensive Evaluation of Dimension Reduction Methods for Data Visualization"☆14Updated last year
- Resources for Machine Learning Explainability☆87Updated last year
- ☆32Updated 9 months ago
- Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep…☆152Updated 5 years ago
- Fast implementation of Venn-ABERS probabilistic predictors☆76Updated last year
- stand alone Neural Additive Models, forked from google-reasearch for easy import to colab☆29Updated 5 years ago
- Here we address the global structure preservation by tSNE and UMAP☆47Updated 5 years ago
- Contrastive neighbor embeddings☆57Updated 3 months ago
- Scikit-Learn compatible transformer that turns categorical variables into dense entity embeddings.☆42Updated 2 years ago
- Understanding the theory behind UMAP☆189Updated 2 weeks ago
- Course for Interpreting ML Models☆52Updated 2 years ago
- ☆17Updated 3 years ago
- Python package for the creation, manipulation, and learning of Causal DAGs☆157Updated 2 years ago
- Repository for code release of paper "Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data" (AISTATS 2020)☆48Updated 5 years ago