InterpretML - Alpha Release

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?
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 3.6+ | Linux, Mac, Windows

pip install 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 lossless 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	.922±.002	*.928±.002*
Heart Disease	Medical	.895±.030	.890±.008	.870±.014	*.916±.010*
Breast Cancer	Medical	*.995±.005*	.992±.009	*.995±.006*	*.995±.006*
Telecom Churn	Business	.804±.015	.824±.002	.850±.006	*.851±.005*
Credit Fraud	Security	.979±.002	.950±.007	*.981±.003*	.975±.005

Notebook for reproducing table

Supported Techniques

Interpretability Technique	Type	Examples
Explainable Boosting	glassbox model	Notebooks
Decision Tree	glassbox model	Notebooks
Decision Rule List	glassbox model	Coming Soon
Linear/Logistic Regression	glassbox model	Notebooks
SHAP Kernel Explainer	blackbox explainer	Notebooks
SHAP Tree Explainer	blackbox explainer	Coming Soon
LIME	blackbox explainer	Notebooks
Morris Sensitivity Analysis	blackbox explainer	Notebooks
Partial Dependence	blackbox explainer	Notebooks

In addition to these, InterpretML is extended by the following repositories:

Interpret-Community: Experimental repository with additional interpretability methods and utility functions to handle real-world datasets and workflows.
Interpret-Text: Supports a collection of interpretability techniques for models trained on text data.

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)

Understand individual predictions

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

And if you have multiple models, compare them

show([logistic_regression, decision_tree])

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!

Citations

InterpretML

"InterpretML: A Unified Framework for Machine Learning Interpretability" (H. Nori, S. Jenkins, P. Koch, and R. Caruana 2019)

@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}
}

juangon/interpret

InterpretML - Alpha Release

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

Let there be light.

Installation

Introducing the Explainable Boosting Machine (EBM)

Supported Techniques

Train a glassbox model

Acknowledgements

Citations

Videos

External links

Papers that use or compare EBMs

Contact us

If a tree fell in your random forest, would anyone notice?