shapley-additive-explanations

Here are 36 public repositories matching this topic...

AstraZeneca / awesome-shapley-value

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

balajissp / dash-shap-components

Star

python3 dash python-packages plotly-dash shap shapley-additive-explanations

Updated Mar 4, 2022
Python

haghish / shapley

Star

Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles

machine-learning machine-learning-algorithms feature-selection feature-extraction class-imbalance shapely feature-importance shap shapley-decomposition shapley-value shapley-values shap-values shapley-additive-explanations shap-analysis class-imbalance-problem shapleyvalue weighted-shapley weighted-shap weighted-shap-confidence-interval weighted-shapley-ci

Updated Apr 2, 2024
R

g-aditi / customer-personality-analysis

Star

Using a Kaggle dataset, customer personality was analysed on the basis of their spending habits, income, education, and family size. K-Means, XGBoost, and SHAP Analysis were performed.

python machine-learning xgboost kmeans kmeans-clustering xgboost-algorithm xgboost-model kmeans-clustering-algorithm shapley-additive-explanations customer-personas shap-analysis customer-personality-analysis

Updated Nov 4, 2021
Jupyter Notebook

LamineTourelab / Explainable-AI

Star

In this repository you will fine explainability of machine learning models.

machine-learning model lime shapely interpretability explainable-ai explainable-ml shapley shap explainability shapley-additive-explanations explainerdashboard shapash

Updated Feb 17, 2023

upunaprosk / la-tda

Star

Code for EACL Workshop paper Can BERT eat RuCoLA? Topological Data Analysis to Explain

glue persistent-homology topological-data-analysis tda ripser shapley-additive-explanations acceptability-judgements barcodes-features

Updated Oct 25, 2023
Jupyter Notebook

anniebritton / Ecological-Drought-ML-Modeling

Star

📊🛰️ Data processing scripts, ML models, and Explainable AI results created as part of my Masters Thesis @ Johns Hopkins

python machine-learning timeseries geospatial-data remote-sensing xgboost drought climate-science google-earth-engine ndvi vegetation-health xai drought-indices drought-monitoring shapley-additive-explanations ecological-drought

Updated Nov 17, 2023
Jupyter Notebook

sachin17git / Malware-detection-ML

Star

Android malware detection using machine learning.

python machine-learning random-forest machine-learning-algorithms feature-selection logistic-regression bayesian-inference support-vector-machines bayesian-optimization multicollinearity k-nearest-neighbours malware-detection voting-classifier shapley-values xgboost-classifier shap-values shapley-additive-explanations catboost-classifier stacking-classifier

Updated Nov 23, 2022
Jupyter Notebook

josedv82 / NBA_Schedule_XGBoost_Classifier

Star

Predicting NBA game outcomes using schedule related information. This is an example of supervised learning where a xgboost model was trained with 20 seasons worth of NBA games and uses SHAP values for model explainability.

h2o supervised-learning xgboost nba-stats nba-analytics h2oai xgboost-algorithm shap shapley-value shapley-additive-explanations

Updated Nov 15, 2021
Jupyter Notebook

Kaushikjas10 / Liquefaction-XGBoost-SHAP-Jas-Dodagoudar

Star

This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.

python machine-learning matlab artificial-intelligence geotechnical-engineering smote boosting synthetic-dataset-generation liquefaction extreme-gradient-boosting explainable-machine-learning shapley-additive-explanations smote-oversampler kmeans-smote

Updated Mar 28, 2024
Jupyter Notebook

jwuphysics / gnn-linking-lengths

Star

Measuring galaxy environmental distance scales with GNNs and explainable ML models

astronomy astrophysics graph-neural-networks explainable-machine-learning shapley-additive-explanations

Updated Feb 14, 2024
Jupyter Notebook

LightnessOfBeing / ImpreciseSHAP

Star

Implementation of the algorithm described in the paper "An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data"

python machine-learning explainable-ai explainable-ml imprecise-probability interpretable-machine-learning shap shapley-value shapley-additive-explanations

Updated Aug 9, 2021
Jupyter Notebook

anishjohnson / Credit_Card_Default_Prediction

Star

In this project we predict credit card defaults using classification models.

machine-learning logistic-regression classification-algorithims knearest-neighbor-classifier random-forest-classifier xgboost-classifier shapley-additive-explanations threshold-moving

Updated Mar 2, 2022
Jupyter Notebook

iamlmn / simpleML

Star

No-code Machine learning (Pre-alpha)

python machine-learning linear-regression classification no-code automated-machine-learning simpleml shapley-additive-explanations

Updated Aug 23, 2022
HTML

wideraHannes / SHAP-In-NLP

Star

Code for my thesis about SHAP. Implementation of DecisionTree, SVM, BERT on 2 Datasets Imdb and Argument Mining

nlp machine-learning sentiment-analysis thesis transformer overview bachelor-thesis bert argument-mining interpretable-deep-learning explainable-ai interpretable-machine-learning shap shapley-additive-explanations

Updated Mar 26, 2024
Jupyter Notebook

yhe-rs / hac-rf-shap

Star

Determining Feature Importance by Integrating Random Forest and SHAP in Python

python principal-component-analysis random-forest-regression shapley-additive-explanations post-hoc-pairwise-test

Updated Mar 2, 2024
Jupyter Notebook

ksharma67 / Partial-Dependent-Plots-Individual-Conditional-Expectation-Plots-With-SHAP

Star

The goal of SHAP is to explain the prediction of an instance x by computing the contribution of each feature to the prediction. The SHAP explanation method computes Shapley values from coalitional game theory. The feature values of a data instance act as players in a coalition.