InterpretML

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In the beginning machines learned in darkness, and data scientists struggled in the void to explain them.

Let there be light.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions.

Interpretability is essential for:

• Model debugging - Why did my model make this mistake?
• Feature Engineering - How can I improve my model?
• Detecting fairness issues - Does my model discriminate?
• Human-AI cooperation - How can I understand and trust the model's decisions?
• Regulatory compliance - Does my model satisfy legal requirements?
• High-risk applications - Healthcare, finance, judicial, ...

Installation

Python 3.7+ | Linux, Mac, Windows

pip install interpret
# OR
conda install -c conda-forge interpret

Introducing the Explainable Boosting Machine (EBM)

EBM is an interpretable model developed at Microsoft Research^*. It uses modern machine learning techniques like bagging, gradient boosting, and automatic interaction detection to breathe new life into traditional GAMs (Generalized Additive Models). This makes EBMs as accurate as state-of-the-art techniques like random forests and gradient boosted trees. However, unlike these blackbox models, EBMs produce exact explanations and are editable by domain experts.

| Dataset/AUROC | Domain | Logistic Regression | Random Forest | XGBoost | Explainable Boosting Machine | |---------------|---------|:-------------------:|:-------------:|:---------------:|:----------------------------:| | Adult Income | Finance | .907±.003 | .903±.002 | .927±.001 | .928±.002 | | Heart Disease | Medical | .895±.030 | .890±.008 | .851±.018 | .898±.013 | | Breast Cancer | Medical | .995±.005 | .992±.009 | .992±.010 | .995±.006 | | Telecom Churn | Business| .849±.005 | .824±.004 | .828±.010 | .852±.006 | | Credit Fraud | Security| .979±.002 | .950±.007 | .981±.003 | .981±.003 |

Notebook for reproducing table

Supported Techniques

| Interpretability Technique | Type | |-----------------------------|--------------------| | Explainable Boosting | glassbox model | | APLR | glassbox model | | Decision Tree | glassbox model | | Decision Rule List | glassbox model | | Linear/Logistic Regression | glassbox model | | SHAP Kernel Explainer | blackbox explainer | | LIME | blackbox explainer | | Morris Sensitivity Analysis | blackbox explainer | | Partial Dependence | blackbox explainer |

Train a glassbox model

Let's fit an Explainable Boosting Machine

from interpret.glassbox import ExplainableBoostingClassifier

ebm = ExplainableBoostingClassifier()
ebm.fit(X_train, y_train)

# or substitute with LogisticRegression, DecisionTreeClassifier, RuleListClassifier, ...
# EBM supports pandas dataframes, numpy arrays, and handles "string" data natively.

Understand the model

from interpret import show

ebm_global = ebm.explain_global()
show(ebm_global)

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Understand individual predictions

ebm_local = ebm.explain_local(X_test, y_test)
show(ebm_local)

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And if you have multiple model explanations, compare them

show([logistic_regression_global, decision_tree_global])

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If you need to keep your data private, use Differentially Private EBMs (see DP-EBMs)

from interpret.privacy import DPExplainableBoostingClassifier, DPExplainableBoostingRegressor

dp_ebm = DPExplainableBoostingClassifier(epsilon=1, delta=1e-5) # Specify privacy parameters
dp_ebm.fit(X_train, y_train)

show(dp_ebm.explain_global()) # Identical function calls to standard EBMs

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For more information, see the documentation.

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EBMs include pairwise interactions by default. For 3-way interactions and higher see this notebook: https://interpret.ml/docs/python/examples/custom-interactions.html

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Interpret EBMs can be fit on datasets with 100 million samples in several hours. For larger workloads consider using distributed EBMs on Azure SynapseML: classification EBMs and regression EBMs

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Acknowledgements

InterpretML was originally created by (equal contributions): Samuel Jenkins, Harsha Nori, Paul Koch, and Rich Caruana

EBMs are fast derivative of GA2M, invented by: Yin Lou, Rich Caruana, Johannes Gehrke, and Giles Hooker

Many people have supported us along the way. Check out ACKNOWLEDGEMENTS.md!

We also build on top of many great packages. Please check them out!

plotly | dash | scikit-learn | lime | shap | salib | skope-rules | treeinterpreter | gevent | joblib | pytest | jupyter

<a name="citations">Citations</a>

<details open> <summary><strong>InterpretML</strong></summary> <hr/> <details open> <summary> <em>"InterpretML: A Unified Framework for Machine Learning Interpretability" (H. Nori, S. Jenkins, P. Koch, and R. Caruana 2019)</em> </summary> <br/> <pre> @article{nori2019interpretml, title={InterpretML: A Unified Framework for Machine Learning Interpretability}, author={Nori, Harsha and Jenkins, Samuel and Koch, Paul and Caruana, Rich}, journal={arXiv preprint arXiv:1909.09223}, year={2019} } </pre> <a href="https://arxiv.org/pdf/1909.09223.pdf">Paper link</a> </details> <hr/> </details> <details> <summary><strong>Explainable Boosting</strong></summary> <hr/> <details> <summary> <em>"Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission" (R. Caruana, Y. Lou, J. Gehrke, P. Koch, M. Sturm, and N. Elhadad 2015)</em> </summary> <br/> <pre> @inproceedings{caruana2015intelligible, title={Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission}, author={Caruana, Rich and Lou, Yin and Gehrke, Johannes and Koch, Paul and Sturm, Marc and Elhadad, Noemie}, booktitle={Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, pages={1721--1730}, year={2015}, organization={ACM} } </pre> <a href="https://www.microsoft.com/en-us/research/wp-content/uploads/2017/06/KDD2015FinalDraftIntelligibleModels4HealthCare_igt143e-caruanaA.pdf">Paper link</a> </details> <details> <summary> <em>"Accurate intelligible models with pairwise interactions" (Y. Lou, R. Caruana, J. Gehrke, and G. Hooker 2013)</em> </summary> <br/> <pre> @inproceedings{lou2013accurate, title={Accurate intelligible models with pairwise interactions}, author={Lou, Yin and Caruana, Rich and Gehrke, Johannes and Hooker, Giles}, booktitle={Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining}, pages={623--631}, year={2013}, organization={ACM} } </pre> <a href="https://www.cs.cornell.edu/~yinlou/papers/lou-