Language Models

Interpretability and explainability of data and machine learning models

The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datasets and machine learning models. The AI Explainability 360 Python package includes a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics. The AI Explainability 360 interactive experience provides a gentle introduction to the concepts and capabilities by walking through an example use case for different consumer personas. The tutorials and example notebooks offer a deeper, data scientist-oriented introduction. […]

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scikit-learn

Interpreting scikit-learn’s decision tree and random forest predictions

Package for interpreting scikit-learn’s decision tree and random forest predictions. Allows decomposing each prediction into bias and feature contribution components as described in http://blog.datadive.net/interpreting-random-forests/. For a dataset with n features, each prediction on the dataset is decomposed as prediction = bias + feature_1_contribution + … + feature_n_contribution. It works on scikit-learn’s DecisionTreeRegressor DecisionTreeClassifier ExtraTreeRegressor ExtraTreeClassifier RandomForestRegressor RandomForestClassifier ExtraTreesRegressor ExtraTreesClassifier Free software: BSD license Dependencies Installation The easiest way to install the package is via pip: $ pip install treeinterpreter Usage […]

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Machine Learning

A library that implements fairness-aware machine learning algorithms

themis-ml is a Python library built on top of pandas and sklearnthat implements fairness-aware machine learning algorithms. themis-ml defines discrimination as the preference (bias) for or against a set of social groups that result in the unfair treatment of its members with respect to some outcome. It defines fairness as the inverse of discrimination, and in the context of a machine learning algorithm, this is measured by the degree to which the algorithm’s predictions favor one social group over another […]

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Interpreter

Python Library for Model Interpretation/Explanations

Skater is a unified framework to enable Model Interpretation for all forms of model to help one build an Interpretable machine learning system often needed for real world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). The project was started […]

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Plot

Python Individual Conditional Expectation Plot Toolbox

Python Individual Conditional Expectation Plot Toolbox A Python implementation of individual conditional expecation plots inspired by R’s ICEbox. Individual conditional expectation plots were introduced in Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation (arXiv:1309.6392). Quickstart pycebox is available on PyPI and can be installed with pip install pycebox. The tutorial recreates the first example in the above paper using pycebox. Development For easy development and prototyping using IPython notebooks, a Docker environment is included. […]

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BreakDown

Python implementation of R package breakDown

Python implementation of breakDown package (https://github.com/pbiecek/breakDown). Docs: https://pybreakdown.readthedocs.io. Requirements Nothing fancy, just python 3.5.2+ and pip. Installation Install directly from github git clone https://github.com/bondyra/pyBreakDown cd ./pyBreakDown python3 setup.py install # (or use pip install . instead) Basic usage Load dataset from sklearn import datasets x = datasets.load_boston() feature_names = x.feature_names Prepare model model = tree.DecisionTreeRegressor() Train model

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Models

Learning to Explain: An Information-Theoretic Perspective on Model Interpretation

Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation at ICML 2018, by Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael I. Jordan. Dependencies The code for L2X runs with Python and requires Tensorflow of version 1.2.1 or higher and Keras of version 2.0 or higher. Please pip install the following packages: numpy tensorflow keras pandas nltk Or you may run the following and in shell to install the required packages: git […]

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Machine Learning

Lime: Explaining the predictions of any machine learning classifier

This project is about explaining what machine learning classifiers (or models) are doing. At the moment, we support explaining individual predictions for text classifiers or classifiers that act on tables (numpy arrays of numerical or categorical data) or images, with a package called lime (short for local interpretable model-agnostic explanations). Lime is based on the work presented in this paper (bibtex here for citation). Here is a link to the promo video: Our plan is to add more packages that […]

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Machine Learning

A Python package which helps to debug machine learning classifiers and explain their predictions

ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions. It provides support for the following machine learning frameworks and packages: scikit-learn. Currently ELI5 allows to explain weights and predictions of scikit-learn linear classifiers and regressors, print decision trees as text or as SVG, show feature importances and explain predictions of decision trees and tree-based ensembles. ELI5 understands text processing utilities from scikit-learn and can highlight text data accordingly. Pipeline and FeatureUnion are supported. […]

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Machine Learning

A game theoretic approach to explain the output of any machine learning model

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations). Install SHAP can be installed from either PyPI or conda-forge: pip install shap or conda install -c conda-forge shap Tree ensemble example (XGBoost/LightGBM/CatBoost/scikit-learn/pyspark models) While SHAP can explain the output of any machine learning model, we […]

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