Projects sharing features with Treeinterpreter

python partial dependence plot toolbox

SHAP is a machine learning explainer that uses a game-theoretic framework to estimate the contribution of each feature to a model prediction. It provides a set of tools for quantifying how individual input features push a specific output away from a baseline value. The project includes specialized explainers for different architectures, including high-speed implementations for decision trees and ensemble models, linearization algorithms for deep learning networks, and covariance integration for linear models. It also features a model-agnostic interpretability tool that uses a kernel method to

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

This project is an agnostic model interpretability framework and explainability tool designed to provide local interpretable explanations for individual predictions. It functions as a local surrogate model that approximates the behavior of any machine learning classifier or regression model to identify the most influential features for a specific instance. The framework is designed to be model-agnostic, meaning it can explain predictions across tabular, text, and image data regardless of the underlying architecture. It employs local linear approximations and feature importance visualization t

XAI - An eXplainability toolbox for machine learning

(a.k.a. Path-Integrated Gradients, a.k.a. Axiomatic Attribution for Deep Networks)

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

A toolbox to iNNvestigate neural networks' predictions!

moDel Agnostic Language for Exploration and eXplanation

Captum is an open-source library for explaining model predictions by attributing them to input features, neurons, and layers using gradient-based and perturbation-based methods. It provides a modular framework for implementing, evaluating, and combining a range of explanation techniques, including gradient-based attribution, perturbation-based analysis, game-theoretic Shapley value approximation, and surrogate model explanations, with support for parallelization and noise stabilization. The library distinguishes itself through its breadth of attribution methods and its support for advanced in

The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).

Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University

Interpretability and explainability of data and machine learning models

DeepLIFT: Deep Learning Important FeaTures

Leave One Feature Out Importance

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

TorchCAM: class activation explorer

NeuroX provide all the necessary tooling to perform Interpretation and Analysis of (Deep) Neural Networks centered around Probing. Specifically, the toolkit provides:

Explain & debug any blackbox machine learning model with a single line of code.

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

A library that implements fairness-aware machine learning algorithms

effector an eXplainable AI package for tabular data. It:

Python implementation of the rulefit algorithm

A Tree based feature selection tool which combines both the Boruta feature selection algorithm with shapley values.