A library for debugging/inspecting machine learning classifiers and explaining their predictions

Last update: Dec 30, 2022

Overview

ELI5

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. It also allows to debug scikit-learn pipelines which contain HashingVectorizer, by undoing hashing.
Keras - explain predictions of image classifiers via Grad-CAM visualizations.
xgboost - show feature importances and explain predictions of XGBClassifier, XGBRegressor and xgboost.Booster.
LightGBM - show feature importances and explain predictions of LGBMClassifier and LGBMRegressor.
CatBoost - show feature importances of CatBoostClassifier, CatBoostRegressor and catboost.CatBoost.
lightning - explain weights and predictions of lightning classifiers and regressors.
sklearn-crfsuite. ELI5 allows to check weights of sklearn_crfsuite.CRF models.

ELI5 also implements several algorithms for inspecting black-box models (see Inspecting Black-Box Estimators):

TextExplainer allows to explain predictions of any text classifier using LIME algorithm (Ribeiro et al., 2016). There are utilities for using LIME with non-text data and arbitrary black-box classifiers as well, but this feature is currently experimental.
Permutation importance method can be used to compute feature importances for black box estimators.

Explanation and formatting are separated; you can get text-based explanation to display in console, HTML version embeddable in an IPython notebook or web dashboards, a pandas.DataFrame object if you want to process results further, or JSON version which allows to implement custom rendering and formatting on a client.

License is MIT.

Check docs for more.

A library for debugging/inspecting machine learning classifiers and explaining their predictions

Related tags

Overview

ELI5

Owner

Bias and Fairness Audit Toolkit

JittorVis - Visual understanding of deep learning model.

Code for "High-Precision Model-Agnostic Explanations" paper

Lime: Explaining the predictions of any machine learning classifier

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

A python library for decision tree visualization and model interpretation.

treeinterpreter - Interpreting scikit-learn's decision tree and random forest predictions.

GNNLens2 is an interactive visualization tool for graph neural networks (GNN).

Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

FairML - is a python toolbox auditing the machine learning models for bias.

A library that implements fairness-aware machine learning algorithms

A collection of infrastructure and tools for research in neural network interpretability.

Visualizer for neural network, deep learning, and machine learning models

Visual analysis and diagnostic tools to facilitate machine learning model selection.

pytorch implementation of "Distilling a Neural Network Into a Soft Decision Tree"

Algorithms for monitoring and explaining machine learning models

Delve is a Python package for analyzing the inference dynamics of your PyTorch model.

tensorboard for pytorch (and chainer, mxnet, numpy, ...)

Auralisation of learned features in CNN (for audio)

🎆 A visualization of the CapsNet layers to better understand how it works