Fit interpretable machine learning models. Explain blackbox machine learning.

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 python package for training interpretable machine learning models and explaining blackbox systems. Interpretability is essential for:

• Model debugging - Why did my model make this mistake?
• Detecting bias - 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, …

Historically, the most interpretable machine learning models were not very accurate, and the most accurate models were not very interpretable. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM)* which has both high accuracy and interpretability. EBM uses modern machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability.

Notebook for reproducing table

In addition to EBM, InterpretML also supports methods like LIME, SHAP, linear models, partial dependence, decision trees and rule lists. The package makes it easy to compare and contrast models to find the best one for your needs.

* EBM is a fast implementation of GA2M. Details on the algorithm can be found here.

Installation

Python 3.5+ | Linux, Mac OS X, Windows

pip install -U interpret

Getting Started

Let’s fit an Explainable Boosting Machine

from interpret.glassbox import ExplainableBoostingClassifier

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

# 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])

Example Notebooks

• Interpretable machine learning models for binary classification
• Interpretable machine learning models for regression
• Blackbox interpretability for binary classification
• Blackbox interpretability for regression

Roadmap

Currently we’re working on:

• R language interface (R is currently a WIP. Basic EBM classification can be done via the ebm_classify & ebm_predict_proba functions, but the predictions are a bit less accurate than in python. No plotting included yet, but other R plotting tools can do a basic job visualizing EBM models)
• Missing Values Support
• Improved Categorical Encoding
• Interaction effect purification (see citations for details)

…and lots more! Get in touch to find out more.

Contributing

If you are interested contributing directly to the code base, please see CONTRIBUTING.md.

Acknowledgements

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

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

Citations

Paper link

External links

• A gentle introduction to GA2Ms, a white box model
• On Model Explainability: From LIME, SHAP, to Explainable Boosting
• Benchmarking and MLI experiments on the Adult dataset
• Dealing with Imbalanced Data (Mortgage loans defaults)
• Kaggle PGA Tour analysis by GAM
• Interpretable Prediction of Goals in Soccer
• Explaining Model Pipelines With InterpretML
• Explain Your Model with Microsoft’s InterpretML

Contact us

There are multiple ways to get in touch:

• Email us at interpret@microsoft.com
• Or, feel free to raise a GitHub issue

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