A game theoretic approach to explain the output of any machine learning model.
-
Updated
Dec 13, 2024 - Jupyter Notebook
#
shap
Here are 371 public repositories matching this topic...
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
data-science machine-learning neural-network random-forest scikit-learn xgboost hyperparameter-optimization lightgbm ensemble feature-engineering decision-tree hyper-parameters automl automated-machine-learning catboost automatic-machine-learning mljar shap tuning-algorithm models-tuning
-
Updated
Nov 26, 2024 - Python
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
python machine-learning transparency lime interpretability ethical-artificial-intelligence explainable-ml shap explainability
-
Updated
Dec 9, 2024 - Jupyter Notebook
Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
dashboard plotly dash data-scientists model-predictions interactive-plots interactive-dashboards xai inner-workings shap explainer xai-library shap-values permutation-importances
-
Updated
Jul 18, 2024 - Python
A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.
feature-selection model-selection xgboost hyperparameter-optimization lightgbm parameter-tuning shap
-
Updated
Jun 8, 2024 - Jupyter Notebook
Fast SHAP value computation for interpreting tree-based models
-
Updated
Jun 26, 2023 - Python
利用lightgbm做(learning to rank)排序学习，包括数据处理、模型训练、模型决策可视化、模型可解释性以及预测等。Use LightGBM to learn ranking, including data processing, model training, model decision visualization, model interpretability and prediction, etc.
-
Updated
Sep 11, 2022 - Python
Shapley Interactions for Machine Learning
python machine-learning interpretability shapley shap explainability feature-interactions shapley-interactions shapiq
-
Updated
Dec 12, 2024 - Python
A power-full Shapley feature selection method.
-
Updated
May 3, 2024 - Python
TimeSHAP explains Recurrent Neural Network predictions.
-
Updated
Dec 21, 2023 - Jupyter Notebook
Automated Tool for Optimized Modelling
visualization python data-science machine-learning scikit-learn modelling model-predictions data-exploration data-pipeline interactive-visualizations automl mlflow shap dagshub
-
Updated
Jul 15, 2024 - HTML
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
data-science machine-learning deep-learning artificial-intelligence lime explainable-artificial-intelligence explainable-ai explainable-ml xai shapley shap explainability shapley-decomposition explainable shapley-value shapley-q-value shapley-additive-explanations owen-value
-
Updated
Aug 8, 2022
Validation (like Recursive Feature Elimination for SHAP) of (multiclass) classifiers & regressors and data used to develop them.
data-science machine-learning statistics data-analysis tree-model multi-class-classification binary-classifiers recursive-feature-elimination shap feature-elimination regressors
-
Updated
Aug 19, 2024 - Python
Explainable Machine Learning in Survival Analysis
machine-learning r variable-importance biostatistics survival-analysis r-package cox-regression cox-model time-to-event censored-data interpretable-ml explainable-ai explainable-ml xai interpretable-machine-learning brier-scores shap explanatory-model-analysis probabilistic-machine-learning explainable-machine-learning
-
Updated
Jun 15, 2024 - R
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
May 6, 2024 - Julia
streamlit-shap provides a wrapper to display SHAP plots in Streamlit.
-
Updated
Jul 21, 2022 - Python
SurvSHAP(t): Time-dependent explanations of machine learning survival models
learning model machine variable-importance biostatistics survival-analysis machinelearning survival cox-regression cox-model time-to-event explainable-ai xai interpretable cph brier-scores shap explainable censored probabilistic-machine-learning
-
Updated
Jan 4, 2024 - Jupyter Notebook
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
Jul 25, 2024 - R
SHAP Plots in R
-
Updated
Dec 13, 2024 - R
Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)
machine-learning paper lime interpretability adversarial-attacks explanations shap post-hoc-explanation fool-lime
-
Updated
Dec 8, 2022 - Jupyter Notebook
Improve this page
Add a description, image, and links to the shap topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the shap topic, visit your repo's landing page and select "manage topics."