259 Open source projects that are alternatives of or similar to ProtoTree

Fit interpretable models. Explain blackbox machine learning.

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

A curated list of awesome machine learning interpretability resources.

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.

Implementation of the paper "Shapley Explanation Networks"

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

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

FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

Fast approximate Shapley values in R

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

For calculating global feature importance using Shapley values.

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

(Explainable AI) - Learning Non-Monotonic Logic Programs From Statistical Models Using High-Utility Itemset Mining

GAM (Global Attribution Mapping) explains the landscape of neural network predictions across subpopulations

Predictive AI layer for existing databases.

Papers about explainability of GNNs

A repository for explaining feature attributions and feature interactions in deep neural networks.

Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers

A lightweight implementation of removal-based explanations for ML models.

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

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

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

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

Code for NeurIPS 2019 paper ``Self-Critical Reasoning for Robust Visual Question Answering''

Molecular Explanation Generator

XAI Tutorial for the Explainable AI track in the ALPS winter school 2021

Collection of NLP model explanations and accompanying analysis tools

Examples of Data Science projects and Artificial Intelligence use cases

Explaining the output of machine learning models with more accurately estimated Shapley values

Concept Bottleneck Models, ICML 2020

Debugging, monitoring and visualization for Python Machine Learning and Data Science

Code release for Your “Flamingo” is My “Bird”: Fine-Grained, or Not (CVPR 2021 Oral)

Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020

Data generator for Arena - interactive XAI dashboard

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

Multi-Label Learning from Single Positive Labels - CVPR 2021

Implementation of CVPR'21: RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction

A tool for visualizing the structure and performance of Random Forests 🌳

Python package to visualize and cluster partial dependence.

📈 Click-Through Rate Prediction using Logistic Regression and Tree Algorithms

Official Implementation of CVPR2021 paper: Continual Learning via Bit-Level Information Preserving

[ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples

Code for "Free-Lunch Saliency via Attention in Atari Agents"

[CVPR2021] Meta Batch-Instance Normalization for Generalizable Person Re-Identification

Python package for tackling multi-class imbalance problems. http://www.cs.put.poznan.pl/mlango/publications/multiimbalance/

A fast Tsetlin Machine implementation employing bit-wise operators, with MNIST demo.

Top 5th percentile solution to the Kaggle knowledge problem - Bike Sharing Demand

Implementations of machine learning algorithm by Python 3

A Julia package for interpretable machine learning with stochastic Shapley values

Variants of Vision Transformer and its downstream tasks

[CVPR 2021 Oral] Im2Vec Synthesizing Vector Graphics without Vector Supervision

PyTorch reimplementation of the paper "Involution: Inverting the Inherence of Convolution for Visual Recognition" (2D and 3D Involution) [CVPR 2021].