interpretml / interpret

Watcher seems to ZMQ server, and WatcherClient is ZMQ Client, but there is no API/Interface to config server IP address.
Do I need to implement a class that inherits from WatcherClient?

Trusted-AI / AIX360

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Interpretability and explainability of data and machine learning models

machine-learning deep-learning artificial-intelligence ibm-research explainable-ai explainable-ml xai ibm-research-ai codait trusted-ai trusted-ml explainabil

Updated May 2, 2021
Python

ModelOriented / DALEX

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moDel Agnostic Language for Exploration and eXplanation

black-box data-science machine-learning predictive-modeling fairness interpretability explainable-artificial-intelligence explanations explainable-ai explainable-ml xai model-visualization interpretable-machine-learning iml dalex responsible-ai responsible-ml explanatory-model-analysis

Updated May 11, 2021
Python

lopusz / awesome-interpretable-machine-learning

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data-science machine-learning interpretable-ai interpretable-ml explainable-ai xai interpretable-machine-learning

Updated Jul 9, 2020
Python

EthicalML / xai

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XAI - An eXplainability toolbox for machine learning

machine-learning ai evaluation ml artificial-intelligence upsampling bias interpretability feature-importance explainable-ai explainable-ml xai imbalance downsampling explainability bias-evaluation machine-learning-explainability xai-library

Updated Apr 23, 2021
Python

maelfabien / Machine_Learning_Tutorials

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Code, exercises and tutorials of my personal blog ! 📝

python machine-learning statistics dataviz ai deep-learning tensorflow example keras tutorials pytorch explainable-ai

Updated Feb 18, 2020
Jupyter Notebook

interpretml / DiCE

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Open

Reduce cyclometric complexity of 'ExplainerBase.is_cf_valid' to less than 10

gaugup commented May 3, 2021

flake8 . --count --max-complexity=10 --max-line-length=127 --statistics
.\dice_ml\explainer_interfaces\explainer_base.py:521:5: C901 'ExplainerBase.is_cf_valid' is too complex (11)

Reduce cyclometric complexity of 'PublicData.init' to less than 10

Open

Reduce cyclometric complexity of 'PrivateData.init' to less than 10

aerdem4 / lofo-importance

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Leave One Feature Out Importance

data-science machine-learning feature-selection feature-importance explainable-ai

Updated Jan 27, 2021
Python

BCG-Gamma / facet

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Human-explainable AI.

python data-science machine-learning statistics simulation model-selection data-analytics hyperparameter-tuning interpretability explainable-ai shap-vector-decomposition

Updated Apr 29, 2021
Jupyter Notebook

cdpierse / transformers-interpret

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

nlp machine-learning natural-language-processing deep-learning neural-network transformers interpretability explainable-ai captum model-explainability transformers-model

Updated May 12, 2021
Python

wangyongjie-ntu / Awesome-explainable-AI

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A collection of research materials on explainable AI/ML

xml interpretability explanation-system interpretable-ai explainable-ai xai counterfactual-explanations recourse

Updated May 12, 2021

ScalaConsultants / Aspect-Based-Sentiment-Analysis

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💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)

machine-learning deep-learning sentiment-analysis tensorflow transformers interpretability aspect-based-sentiment-analysis explainable-ai explainable-ml distill bert-embeddings transformer-models

Updated May 8, 2021
Python

explainX / explainx

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Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.

machine-learning scikit-learn transparency blackbox bias interpretability explainable-artificial-intelligence interpretable-ai explainable-ai explainable-ml xai interpretable-machine-learning machine-learning-interpretability explainability aws-sagemaker explainx

Updated Feb 7, 2021
Jupyter Notebook

ModelOriented / modelStudio

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📍 Interactive Studio for Explanatory Model Analysis

machine-learning predictive-modeling interactive-visualizations interpretability explainable-artificial-intelligence explainable-ai explainable-ml xai model-visualization interpretable-machine-learning iml explainability explanatory-model-analysis explainable-machine-learning

Updated Apr 25, 2021
R

idealo / cnn-exposed

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🕵️‍♂️ Interpreting Convolutional Neural Network (CNN) Results.

machine-learning computer-vision deep-learning neural-network explainable-ai interpretable-machine-learning

Updated Mar 29, 2019
Jupyter Notebook

microsoft / responsible-ai-widgets

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

visualization data-science machine-learning widget ui jupyter widgets ml machinelearning fairness error-analysis explainable-ai explainable-ml fairness-ai explainability fairness-ml responsible-ai

Updated May 13, 2021
TypeScript

suinleelab / path_explain

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

machine-learning tensorflow interpretable-deep-learning explainable-ai

Updated Mar 18, 2021
Jupyter Notebook

mdipietro09 / DataScience_ArtificialIntelligence_Utils

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Examples of Data Science projects and Artificial Intelligence use cases

python data-science machine-learning natural-language-processing reinforcement-learning computer-vision deep-learning time-series examples regression data-visualization artificial-intelligence classification explainable-ai explainable-ml

Updated Dec 21, 2020
Jupyter Notebook

zxhuang1698 / interpretability-by-parts

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Code repository for "Interpretable and Accurate Fine-grained Recognition via Region Grouping", CVPR 2020 (Oral)

pytorch celeba interpretability celeba-dataset fine-grained-classification explainable-ai face-segmentation pytorch-implementation cub-dataset part-based-models weakly-supervised-segmentation weakly-supervised-localization cvpr2020 cvpr-2020 cvpr-oral

Updated Aug 27, 2020
Python

jacobgil / vit-explain

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Explainability for Vision Transformers

deep-learning pytorch transformer explainable-ai vision-transformer

Updated Apr 3, 2021
Python

csinva / hierarchical-dnn-interpretations

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

python data-science machine-learning statistics deep-neural-networks ai deep-learning neural-network jupyter-notebook ml pytorch artificial-intelligence convolutional-neural-networks acd interpretation iclr interpretability feature-importance explainable-ai explainability

Updated Mar 14, 2021
Jupyter Notebook

benedekrozemberczki / shapley

Sponsor Star

A data-driven approach to quantify the value of classifiers in a machine learning ensemble.

classifier machine-learning deep-learning random-forest ensemble ensemble-learning game-theory voting-classifier random-forest-classifier explainable-ai explainable-ml weighted-voting-games shapley owen shapley-value game-theory-toolbox voting-game

Updated Apr 11, 2021
Python

d909b / cxplain

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Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.

machine-learning deep-learning interpretability explainable-ai explainable-ml explainability

Updated Aug 22, 2020
Python

shijx12 / XNM-Net

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Pytorch implementation of "Explainable and Explicit Visual Reasoning over Scene Graphs "

scene-graph clevr visual-reasoning explainable-ai neural-module-networks cvpr2019

Updated Mar 17, 2019
Python

declare-lab / dialogue-understanding

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This repository contains PyTorch implementation for the baseline models from the paper Utterance-level Dialogue Understanding: An Empirical Study

natural-language-processing sentiment-analysis dialogue pytorch lstm pretrained-models emotion-analysis bert dialogue-systems conversational-agents emotion-recognition natural-language-understanding conversational-ai adversarial-attacks explainable-ai dialogue-act emotion-recognition-in-conversation bert-embeddings dialogue-understanding utterance-level-dialogue

Updated Nov 24, 2020
Python

laura-rieger / deep-explanation-penalization

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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

python data-science machine-learning ai deep-learning neural-network jupyter-notebook ml pytorch artificial-intelligence convolutional-neural-network fairness interpretability cdep feature-importance recurrent-neural-network interpretable-deep-learning explainable-ai explainability fairness-ml

Updated Mar 22, 2021
Jupyter Notebook

andreysharapov / xaience

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All about explainable AI, algorithmic fairness and more

interpretability explainable-artificial-intelligence interpretable-deep-learning interpretable-ai explainable-ai explainable-ml interpretable-machine-learning

Updated Apr 18, 2021

NorskRegnesentral / shapr

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

rcpp rstats rcpparmadillo explainable-ai explainable-ml shapley

Updated Apr 22, 2021
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explainable-ai

Here are 268 public repositories matching this topic...

interpretml / interpret

Interpret

mindsdb / mindsdb

microsoft / tensorwatch

How to use Watcher / WatcherClient over tcp/ip network? (ex. cloud - local)

Trusted-AI / AIX360

ModelOriented / DALEX

lopusz / awesome-interpretable-machine-learning

EthicalML / xai

maelfabien / Machine_Learning_Tutorials

interpretml / DiCE

Reduce cyclometric complexity of 'ExplainerBase.is_cf_valid' to less than 10

Reduce cyclometric complexity of 'PublicData.init' to less than 10

Reduce cyclometric complexity of 'PrivateData.init' to less than 10

aerdem4 / lofo-importance

BCG-Gamma / facet

cdpierse / transformers-interpret

wangyongjie-ntu / Awesome-explainable-AI

ScalaConsultants / Aspect-Based-Sentiment-Analysis

explainX / explainx

ModelOriented / modelStudio

idealo / cnn-exposed

microsoft / responsible-ai-widgets

suinleelab / path_explain

mdipietro09 / DataScience_ArtificialIntelligence_Utils

zxhuang1698 / interpretability-by-parts

jacobgil / vit-explain

csinva / hierarchical-dnn-interpretations

benedekrozemberczki / shapley

d909b / cxplain

shijx12 / XNM-Net

declare-lab / dialogue-understanding

laura-rieger / deep-explanation-penalization

andreysharapov / xaience

NorskRegnesentral / shapr

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explainable-ai

Here are 268 public repositories matching this topic...

interpretml / interpret

Interpret

mindsdb / mindsdb

microsoft / tensorwatch

How to use Watcher / WatcherClient over tcp/ip network? (ex. cloud - local)

Trusted-AI / AIX360

ModelOriented / DALEX

lopusz / awesome-interpretable-machine-learning

EthicalML / xai

maelfabien / Machine_Learning_Tutorials

interpretml / DiCE

Reduce cyclometric complexity of 'ExplainerBase.is_cf_valid' to less than 10

Reduce cyclometric complexity of 'PublicData.__init__' to less than 10

Reduce cyclometric complexity of 'PrivateData.__init__' to less than 10

aerdem4 / lofo-importance

BCG-Gamma / facet

cdpierse / transformers-interpret

wangyongjie-ntu / Awesome-explainable-AI

ScalaConsultants / Aspect-Based-Sentiment-Analysis

explainX / explainx

ModelOriented / modelStudio

idealo / cnn-exposed

microsoft / responsible-ai-widgets

suinleelab / path_explain

mdipietro09 / DataScience_ArtificialIntelligence_Utils

zxhuang1698 / interpretability-by-parts

jacobgil / vit-explain

csinva / hierarchical-dnn-interpretations

benedekrozemberczki / shapley

d909b / cxplain

shijx12 / XNM-Net

declare-lab / dialogue-understanding

laura-rieger / deep-explanation-penalization

andreysharapov / xaience

NorskRegnesentral / shapr

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Reduce cyclometric complexity of 'PublicData.init' to less than 10

Reduce cyclometric complexity of 'PrivateData.init' to less than 10