diff --git a/README.md b/README.md index 3baf7e0f6..69216426a 100644 --- a/README.md +++ b/README.md @@ -596,6 +596,7 @@ We also build on top of many great packages. Please check them out! # External links +- [Explainable AI: unlocking value in FEC operations](https://analytiqal.nl/2024/01/22/fec-value-from-explainable-ai/) - [Interpretable Machine Learning – Increase Trust and Eliminate Bias](https://ficonsulting.com/insight-post/interpretable-machine-learning-increase-trust-and-eliminate-bias/) - [Machine Learning Interpretability in Banking: Why It Matters and How Explainable Boosting Machines Can Help](https://www.prometeia.com/en/trending-topics-article/machine-learning-interpretability-in-banking-why-it-matters-and-how-explainable-boosting-machines-can-help) - [Interpretable or Accurate? Why Not Both?](https://towardsdatascience.com/interpretable-or-accurate-why-not-both-4d9c73512192) @@ -615,6 +616,7 @@ We also build on top of many great packages. Please check them out! # Papers that use or compare EBMs +- [DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine](https://arxiv.org/pdf/2402.06885.pdf) - [Distill knowledge of additive tree models into generalized linear models](https://detralytics.com/wp-content/uploads/2023/10/Detra-Note_Additive-tree-ensembles.pdf) - [Explainable Boosting Machines with Sparsity - Maintaining Explainability in High-Dimensional Settings](https://arxiv.org/abs/2311.07452) - [Cost of Explainability in AI: An Example with Credit Scoring Models](https://link.springer.com/chapter/10.1007/978-3-031-44064-9_26) @@ -708,6 +710,7 @@ We also build on top of many great packages. Please check them out! - [On the Physical Nature of Lya Transmission Spikes in High Redshift Quasar Spectra](https://arxiv.org/pdf/2401.04762.pdf) - [GRAND-SLAMIN’ Interpretable Additive Modeling with Structural Constraints](https://openreview.net/pdf?id=F5DYsAc7Rt) - [Identification of groundwater potential zones in data-scarce mountainous region using explainable machine learning](https://www.sciencedirect.com/science/article/pii/S0022169423013598) + # Books that cover EBMs - [Machine Learning for High-Risk Applications](https://www.oreilly.com/library/view/machine-learning-for/9781098102425/) diff --git a/scripts/release_process.txt b/scripts/release_process.txt index 759526ba1..72f6a2ae5 100644 --- a/scripts/release_process.txt +++ b/scripts/release_process.txt @@ -39,7 +39,7 @@ - conda env remove --name interpret_bdist && conda create --yes --name interpret_bdist python=3.10 && conda activate interpret_bdist - pip install interpret_core-*-py3-none-any.whl[debug,notebook,plotly,lime,sensitivity,shap,linear,treeinterpreter,dash,skoperules,testing] - cd - - cd examples/python + - cd docs/interpret/python/examples - pip install jupyter - jupyter notebook - open all the example notebooks, run them, and check the visualizations @@ -57,7 +57,7 @@ set_visualize_provider(InlineProvider()) - IN WINDOWS: get the Visual studio environment with: "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvars64.bat" - pip install interpret-core-*.tar.gz[debug,notebook,plotly,lime,sensitivity,shap,linear,treeinterpreter,dash,skoperules,testing] - cd - - cd examples/python/ + - cd docs/interpret/python/examples - pip install jupyter - jupyter notebook - open all the example notebooks, run them, and check the visualizations @@ -173,7 +173,7 @@ set_visualize_provider(InlineProvider()) - conda env remove --name interpret_conda && conda create --yes --name interpret_conda python=3.10 && conda activate interpret_conda - conda install --yes -c conda-forge interpret-core psutil ipykernel ipython plotly lime SALib shap dill dash dash-core-components dash-html-components dash-table dash_cytoscape gevent requests - cd - - cd examples/python + - cd docs/interpret/python/examples - pip install jupyter - jupyter notebook - open all the example notebooks, run them, and check the visualizations @@ -196,21 +196,21 @@ set_visualize_provider(InlineProvider()) https://pypi.org/project/interpret/#files - test PyPI release on colab: - - https://githubtocolab.com/interpretml/interpret/blob/develop/examples/python/Interpretable_Classification_Methods.ipynb - - https://githubtocolab.com/interpretml/interpret/blob/develop/examples/python/Interpretable_Regression_Methods.ipynb - - https://githubtocolab.com/interpretml/interpret/blob/develop/examples/python/Differentially_Private_EBMs.ipynb - - https://githubtocolab.com/interpretml/interpret/blob/develop/examples/python/Merging_EBM_Models.ipynb - - https://githubtocolab.com/interpretml/interpret/blob/develop/examples/python/EBM_Importances.ipynb - - https://githubtocolab.com/interpretml/interpret/blob/develop/examples/python/Explaining_Blackbox_Classifiers.ipynb - - https://githubtocolab.com/interpretml/interpret/blob/develop/examples/python/Explaining_Blackbox_Regressors.ipynb - - https://githubtocolab.com/interpretml/interpret/blob/develop/examples/python/Prototype_Selection_with_SPOTgreedy.ipynb + - https://githubtocolab.com/interpretml/interpret/blob/develop/docs/interpret/python/examples/Interpretable_Classification_Methods.ipynb + - https://githubtocolab.com/interpretml/interpret/blob/develop/docs/interpret/python/examples/Interpretable_Regression_Methods.ipynb + - https://githubtocolab.com/interpretml/interpret/blob/develop/docs/interpret/python/examples/Differentially_Private_EBMs.ipynb + - https://githubtocolab.com/interpretml/interpret/blob/develop/docs/interpret/python/examples/Merging_EBM_Models.ipynb + - https://githubtocolab.com/interpretml/interpret/blob/develop/docs/interpret/python/examples/EBM_Importances.ipynb + - https://githubtocolab.com/interpretml/interpret/blob/develop/docs/interpret/python/examples/Explaining_Blackbox_Classifiers.ipynb + - https://githubtocolab.com/interpretml/interpret/blob/develop/docs/interpret/python/examples/Explaining_Blackbox_Regressors.ipynb + - https://githubtocolab.com/interpretml/interpret/blob/develop/docs/interpret/python/examples/Prototype_Selection_with_SPOTgreedy.ipynb - test PyPI release locally: - open anaconda console window - conda env remove --name interpret_pypi && conda create --yes --name interpret_pypi python=3.10 && conda activate interpret_pypi - pip install interpret lime # remove lime if we remove lime from example notebooks - cd - - cd examples/python + - cd docs/interpret/python/examples - pip install jupyter - jupyter notebook - open all the example notebooks, run them, and check the visualizations