Topic: "machine-learning-interpretability"
jphall663/awesome-machine-learning-interpretability
A curated list of awesome responsible machine learning resources.
Size: 4.09 MB - Last synced at: 10 days ago - Pushed at: 13 days ago - Stars: 3,757 - Forks: 599
jphall663/interpretable_machine_learning_with_python
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
Language: Jupyter Notebook - Size: 34.7 MB - Last synced at: 9 days ago - Pushed at: 10 months ago - Stars: 676 - Forks: 207
h2oai/mli-resources
H2O.ai Machine Learning Interpretability Resources
Language: Jupyter Notebook - Size: 65.8 MB - Last synced at: about 1 year ago - Pushed at: over 4 years ago - Stars: 477 - Forks: 134
explainX/explainx
Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ [email protected]
Language: Jupyter Notebook - Size: 61.3 MB - Last synced at: 9 days ago - Pushed at: 8 months ago - Stars: 430 - Forks: 56
jphall663/diabetes_use_case
Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
Language: Jupyter Notebook - Size: 73.7 MB - Last synced at: 18 days ago - Pushed at: 10 months ago - Stars: 27 - Forks: 13
SDM-TIB/InterpretME
An interpretable machine learning pipeline over knowledge graphs
Language: Jupyter Notebook - Size: 8.77 MB - Last synced at: 11 days ago - Pushed at: about 1 year ago - Stars: 27 - Forks: 2
jphall663/hc_ml
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
Language: TeX - Size: 34.4 MB - Last synced at: 14 days ago - Pushed at: over 5 years ago - Stars: 22 - Forks: 8
DiegoUsaiUK/Propensity_Modelling
Predicting the Likelihood to Purchase a Financial Product Following a Direct Marketing Campaign
Language: R - Size: 34.2 MB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 21 - Forks: 11
navdeep-G/interpretable-ml
Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
Language: Jupyter Notebook - Size: 84.4 MB - Last synced at: 14 days ago - Pushed at: almost 3 years ago - Stars: 21 - Forks: 8
12wang3/mllp
The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
Language: Python - Size: 3.69 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 19 - Forks: 6
h2oai/article-information-2019
Article for Special Edition of Information: Machine Learning with Python
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jphall663/jsm_2018_paper
Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539
Language: TeX - Size: 12.7 MB - Last synced at: 18 days ago - Pushed at: over 6 years ago - Stars: 9 - Forks: 2
poloclub/telegam
TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning
Language: JavaScript - Size: 4.18 MB - Last synced at: 11 months ago - Pushed at: over 5 years ago - Stars: 7 - Forks: 2
hayesall/bn-rule-extraction
Rule Extraction from Bayesian Networks
Language: Python - Size: 169 KB - Last synced at: 6 days ago - Pushed at: over 3 years ago - Stars: 6 - Forks: 3
vanderschaarlab/INVASE
INVASE: Instance-wise Variable Selection . For more details, read the paper "INVASE: Instance-wise Variable Selection using Neural Networks," International Conference on Learning Representations (ICLR), 2019.
Language: Python - Size: 110 KB - Last synced at: 17 days ago - Pushed at: over 2 years ago - Stars: 5 - Forks: 1
SDM-TIB/InterpretME_Demo
Demonstration of InterpretME, an interpretable machine learning pipeline
Language: Jupyter Notebook - Size: 3.7 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0
xmlx-dev/.github
XMLX GitHub configuration
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nilsdenter/novelty_value_ml
This project contains the data, code and results used in the paper title "On the relationship of novelty and value in digitalization patents: A machine learning approach".
Language: Python - Size: 45.5 MB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0
xmlx-io/.github
XMLX GitHub configuration
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tommykangdra/Credit-Default-Risk
Default Risk Prediction from bank dataset with Interpretable Machine Learning
Language: Jupyter Notebook - Size: 1.35 MB - Last synced at: 6 months ago - Pushed at: almost 4 years ago - Stars: 0 - Forks: 0
