iml

Here are 55 public repositories matching this topic...

interpretml / interpret

Fit interpretable models. Explain blackbox machine learning.

machine-learning ai scikit-learn artificial-intelligence transparency blackbox bias differential-privacy gradient-boosting interpretability interpretable-ai interpretable-ml explainable-ai explainable-ml xai interpretable-machine-learning iml explainability interpretml

Updated Mar 18, 2024
C++

ModelOriented / DALEX

Star

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 Feb 28, 2024
Python

jphall663 / interpretable_machine_learning_with_python

Star

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

Updated Feb 27, 2023
Jupyter Notebook

h2oai / mli-resources

Star

H2O.ai Machine Learning Interpretability Resources

Updated Dec 12, 2020
Jupyter Notebook

ModelOriented / modelStudio

Star

📍 Interactive Studio for Explanatory Model Analysis

visualization learning r ai interactive model machine human interactivity interpretability explainable-ai xai model-visualization interpretable interpretable-machine-learning iml explainable explanatory-model-analysis explainable-machine-learning

Updated Aug 31, 2023
R

hbaniecki / adversarial-explainable-ai

Star

💡 Adversarial attacks on explanations and how to defend them

Updated Mar 5, 2024

pbiecek / breakDown

Sponsor

Star

Model Agnostics breakDown plots

data-science machine-learning interpretability xai visual-explanations iml

Updated Mar 12, 2024
R

The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"

scalability representation-learning nips interpretability interpretable-ai interpretable-ml explainable-ai explainable-ml xai interpretable-machine-learning iml explainability neurips interpretable-classification rule-based-model neuro-symbolic-ai

Updated Mar 12, 2024
Python

ModelOriented / iBreakDown

Star

Break Down with interactions for local explanations (SHAP, BreakDown, iBreakDown)

breakdown interpretability xai shapley iml

Updated Nov 30, 2023
R

nredell / ShapML.jl

Star

A Julia package for interpretable machine learning with stochastic Shapley values

julia feature-importance shapley interpretable-machine-learning iml shap shapley-value stochastic-shapley-values

Updated Feb 21, 2022
Julia

ModelOriented / treeshap

Star

Compute SHAP values for your tree-based models using the TreeSHAP algorithm

machine-learning interpretability explainable-artificial-intelligence explainable-ai xai interpretable-machine-learning iml shap explainability shapley-value responsible-ml explanatory-model-analysis

Updated Jan 23, 2024
R

nredell / shapFlex

Star

An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model

package machine-learning r ensemble r-package causality causal-inference feature-importance causal-networks shapley interpretable-machine-learning iml shap shapley-value shapley-values

Updated Jun 9, 2020
R

rehmanzafar / xai-iml-sota

Star

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

machine-learning human-in-the-loop interpretability explainable-artificial-intelligence researchers interactive-machine-learning deep-learning-visualization human-in-the-loop-machine-learning explainable-ml xai interpretable interpretable-machine-learning iml model-interpretability explainable interpretable-models explainable-models interpretable-learning explaining-ai explanation-methods

Updated Sep 29, 2022
R

automl / DeepCAVE

Star

An interactive framework to visualize and analyze your AutoML process in real-time.

visualization real-time analysis interactive hyperparameters automl sampling-bias iml hyperparameter-importance

Updated Mar 11, 2024
Python

dddb11 / MVSS-Net

Star

Unofficial implementation of MVSS-Net (ICCV 2021) with Pytorch including training code.

pytorch unofficial iml mvss-net

Updated Sep 26, 2023
Python

ModelOriented / live

Star

Local Interpretable (Model-agnostic) Visual Explanations - model visualization for regression problems and tabular data based on LIME method. Available on CRAN

machine-learning lime interpretability xai model-visualization visual-explanations iml