A unified approach to explain the output of any machine learning model.
#
interpretability
Repositories 58
A collection of infrastructure and tools for research in neural network interpretability.
A curated list of awesome machine learning interpretability resources.
fairness
xai
interpretability
iml
fatml
accountability
transparency
machine-learning
data-science
data-mining
python
r
awesome
awesome-list
machine-learning-interpretability
interpretable-machine-learning
interpretable-ml
interpretable-ai
interpretable-deep-learning
explainable-ml
Updated Mar 10, 2019
[ICCV 2017] Torch code for Grad-CAM
iccv17
grad-cam
interpretability
convolutional-neural-networks
deep-learning
heatmap
visual-explanation
Lua
Updated Mar 3, 2017
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
machine-learning
mlops
interpretability
explainability
responsible-ai
responsible-ml
deep-learning
machine-learning-operations
ml-ops
ml-operations
privacy-preserving
privacy
privacy-preserving-ml
privacy-preserving-machine-learning
data-mining
large-scale-ml
production-ml
large-scale-machine-learning
production-machine-learning
Updated Mar 17, 2019
Descriptive mAchine Learning EXplanations
Public facing deeplift repo
deeplift
sensitivity-analysis
integrated-gradients
lrp
guided-backpropagation
saliency-map
interpretable-deep-learning
interpretability
Python
Updated Mar 8, 2019
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anom…
ensemble-learning
active-learning
anomaly-detection
rnn
lstm
explaination
interpretability
time-series
timeseries
trees
unsuperivsed
autoencoder
streaming
concept-drift
generative-adversarial-network
gan
anogan
graph-convolutional-networks
adversarial-attacks
nettack
Python
Updated Feb 19, 2019
H2O.ai Machine Learning Interpretability Resources
machine-learning
python
jupyter-notebooks
interpretability
data-science
data-mining
h2o
mli
xai
fatml
transparency
accountability
fairness
xgboost
machine-learning-interpretability
iml
interpretable-machine-learning
interpretable-ml
interpretable-ai
explainable-ml
Jupyter Notebook
Updated Mar 5, 2019
Practical techniques for interpreting machine learning models.
machine-learning
python
fatml
xai
gradient-boosting-machine
decision-tree
data-science
fairness
interpretable-machine-learning
interpretability
machine-learning-interpretability
iml
accountability
transparency
data-mining
interpretable-ml
interpretable
interpretable-ai
lime
h2o
Jupyter Notebook
Updated Mar 22, 2019
XAI - An eXplainability toolbox for machine learning
explainability
xai
ml
ai
bias
artificial-intelligence
bias-evaluation
explainable-ai
explainable-ml
machine-learning
machine-learning-explainability
interpretability
xai-library
evaluation
imbalance
upsampling
downsampling
feature-importance
Python
Updated Mar 4, 2019
Model Agnostics breakDown plots
R
Updated Mar 2, 2019
Interesting resources related to XAI (Explainable Artificial Intelligence)
R
Updated Mar 20, 2019
Layer-wise Relevance Propagation (LRP) for LSTMs
Python
Updated Dec 21, 2018
⬛ Python Individual Conditional Expectation Plot Toolbox
Jupyter Notebook
Updated Jan 25, 2018
A user interface to interpret machine learning models.
CSS
Updated Oct 19, 2017
Pytorch Implementation of recent visual attribution methods for model interpretability
pytorch
saliency
xai
interpretability
visual-explanations
interpretable-deep-learning
excitation-backpropagation
model-interpretability
Jupyter Notebook
Updated Jan 23, 2019
[ECCV 2018] code for Choose Your Neuron: Incorporating Domain Knowledge Through Neuron Importance
Python
Updated Aug 8, 2018
Automatic equation building and curve fitting. Runs on Tensorflow. Built for academia and research.
machine-learning
tensorflow
curve-fitting
optimization
equation-solver
research-tool
symbolic-computation
academia
symbolic-regression
interpretability
simulation-framework
Jupyter Notebook
Updated Apr 14, 2018
Text classification models. Used a submodule for other projects.
Jupyter Notebook
Updated Sep 19, 2018
BayesGrad: Explaining Predictions of Graph Convolutional Networks
deep-learning
chemistry
neural-network
graph-convolutional-networks
chainer
python
saliency
interpretability
Jupyter Notebook
Updated Aug 2, 2018
Local Interpretable (Model-agnostic) Visual Explanations - model visualization for regression problems and tabular da…
R
Updated Feb 28, 2019
Code for SPINE - Sparse Interpretable Neural Embeddings. Jhamtani H.*, Pruthi D.*, Subramanian A.*, Berg-Kirkpatrick …
Python
Updated Oct 20, 2018
Code for using / reproducing ACD (ICLR 2019) from the paper "Hierarchical interpretations for neural network predicti…
interpretability
neural-network
machine-learning
convolutional-neural-networks
pytorch
interpretation
explainability
Jupyter Notebook
Updated Feb 27, 2019
NeurIPS 2018. Linear-time model comparison tests.
Jupyter Notebook
Updated Nov 10, 2018
Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
python
xgboost
healthcare
interpretability
xai
iml
transparency
machine-learning
data-science
data-mining
machine-learning-interpretability
interpretable-ml
interpretable-machine-learning
explainable-ml
Jupyter Notebook
Updated Sep 7, 2018
Python package to visualize and cluster partial dependence.
Jupyter Notebook
Updated Feb 16, 2018
Towards Understanding Deep Learning Representations via Interactive Experimentation
Python
Updated May 5, 2017
OncoText is an information extraction service for breast pathology reports. It supports over 20 categories including …
Python
Updated Oct 22, 2018
Visualization tool for interpreting NLP models
Python
Updated Dec 27, 2018