shapley-additive-explanations

There are 2 repositories under shapley-additive-explanations topic.

awesome-shapley-value
AstraZeneca / awesome-shapley-value
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
artificial-intelligence deep-learning machine-learning shapley shapley-additive-explanations shapley-value xai data-science explainability explainable lime shap explainable-ai explainable-artificial-intelligence explainable-ml shapley-decomposition shapley-q-value owen-value
136
haghish / shapley
Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles
machine-learning machine-learning-algorithms shap-analysis shap-values shapely class-imbalance class-imbalance-problem feature-extraction feature-importance feature-selection shap shapley-additive-explanations shapley-decomposition shapley-value shapley-values shapleyvalue weighted-shap weighted-shap-confidence-interval weighted-shapley weighted-shapley-ci
Language:R 11
balajissp / dash-shap-components
plotly-dash shap shapley-additive-explanations python3 python-packages dash
Language:Python 9
anniebritton / Ecological-Drought-ML-Modeling
📊🛰️ Data processing scripts, ML models, and Explainable AI results created as part of my Masters Thesis @ Johns Hopkins
climate-science drought drought-indices geospatial-data google-earth-engine machine-learning python remote-sensing shapley-additive-explanations timeseries xgboost xai ecological-drought drought-monitoring ndvi vegetation-health
Language:Jupyter Notebook 5
g-aditi / customer-personality-analysis
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.
machine-learning python kmeans-clustering kmeans xgboost xgboost-algorithm kmeans-clustering-algorithm xgboost-model shap-analysis shapley-additive-explanations customer-personality-analysis customer-personas
Language:Jupyter Notebook 5
LamineTourelab / Explainable-AI
In this repository you will fine explainability of machine learning models.
explainability explainable-ai explainable-ml explainerdashboard lime model shapash shapely interpretability machine-learning shap shapley shapley-additive-explanations
5
upunaprosk / la-tda
Code for EACL Workshop paper Can BERT eat RuCoLA? Topological Data Analysis to Explain
persistent-homology shapley-additive-explanations topological-data-analysis acceptability-judgements barcodes-features glue ripser tda
Language:Jupyter Notebook 5
jwuphysics / gnn-linking-lengths
Measuring galaxy environmental distance scales with GNNs and explainable ML models
astronomy astrophysics explainable-machine-learning graph-neural-networks shapley-additive-explanations
Language:Jupyter Notebook 4
carlacodes / boostmodels
gradient-boosted regression and decision tree models on behavioural animal data (PLOS Computational Biology, doi: https://doi.org/10.1371/journal.pcbi.1011985)
behavior lightgbm machine-learning neuroscience pandas python regression shap shapley-additive-explanations sklearn statsmodels
Language:Python 3
NBA_Schedule_XGBoost_Classifier
josedv82 / NBA_Schedule_XGBoost_Classifier
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.
xgboost xgboost-algorithm h2oai h2o shap shapley-value shapley-additive-explanations nba-stats nba-analytics supervised-learning
Language:Jupyter Notebook 3
Kaushikjas10 / Liquefaction-XGBoost-SHAP-Jas-Dodagoudar
This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.
artificial-intelligence boosting extreme-gradient-boosting geotechnical-engineering liquefaction machine-learning matlab python shapley-additive-explanations explainable-machine-learning kmeans-smote smote smote-oversampler synthetic-dataset-generation
Language:Jupyter Notebook 3
LightnessOfBeing / ImpreciseSHAP
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 shap machine-learning explainable-ai explainable-ml shapley-value shapley-additive-explanations interpretable-machine-learning imprecise-probability
Language:Jupyter Notebook 3
sachin17git / Malware-detection-ML
Android malware detection using machine learning.
bayesian-inference bayesian-optimization feature-selection machine-learning machine-learning-algorithms malware-detection multicollinearity python random-forest shap-values shapley-additive-explanations shapley-values stacking-classifier voting-classifier catboost-classifier k-nearest-neighbours logistic-regression support-vector-machines xgboost-classifier
Language:Jupyter Notebook 3
Akash-07DL / XGB_SHAP_Explainable_AI-Census_income_dataset-
XGB - SHAP XAI
machine-learning machine-learning-algorithms shapley-additive-explanations
Language:Jupyter Notebook 2
anishjohnson / Credit_Card_Default_Prediction
In this project we predict credit card defaults using classification models.
machine-learning threshold-moving classification-algorithims xgboost-classifier random-forest-classifier logistic-regression knearest-neighbor-classifier shapley-additive-explanations
Language:Jupyter Notebook 2
iamlmn / simpleML
No-code Machine learning (Pre-alpha)
linear-regression machine-learning classification simpleml shapley-additive-explanations python no-code automated-machine-learning
Language:HTML 2
ksharma67 / Partial-Dependent-Plots-Individual-Conditional-Expectation-Plots-With-SHAP
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.
eda individual-conditional-expectation matplotlib numpy pandas partial-dependence-plot python seaborn shap sklearn xgboost shapley-additive-explanations
Language:Jupyter Notebook 2
wideraHannes / SHAP-In-NLP
Code for my thesis about SHAP. Implementation of DecisionTree, SVM, BERT on 2 Datasets Imdb and Argument Mining
argument-mining bachelor-thesis bert machine-learning nlp overview sentiment-analysis shap shapley-additive-explanations thesis transformer explainable-ai interpretable-deep-learning interpretable-machine-learning
Language:Jupyter Notebook 2
yhe-rs / hac-rf-shap
Determining Feature Importance by Integrating Random Forest and SHAP in Python
principal-component-analysis python random-forest-regression shapley-additive-explanations post-hoc-pairwise-test
Language:Jupyter Notebook 2
arpansharma-11 / XAI_breastCancer
XAI analytics to understand the working of SHAP values
explainable-ai shapley-additive-explanations
Language:Jupyter Notebook 1
arpansharma-11 / XAI_heartDataset
XAI analytics to understand the working of SHAP values
explainable-ai shapley-additive-explanations xai
Language:Jupyter Notebook 1
datatrigger / interpretable_machine_learning
Getting explanations for predictions made by black box models.
interpretable-machine-learning interpretable-ml explainable-machine-learning explainability interpretability black-box-model shap shapley-additive-explanations lime feature-importance surrogate-models
Language:Jupyter Notebook 1
DQ4781 / LF-ShapAnalysis
An Analysis of Lassa Fever Outbreaks in Nigeria using Machine Learning Models and Shapley Values
computational-epidemiology machine-learning shapley-additive-explanations xgboost random-forest-classifier lassa-fever nigeria
Language:Jupyter Notebook 1
himasai97 / ML_Approaches
ML implementations in Multi-scale model for lignin biosynthesis in Populus Trichocarpa
lignin-biosynthesis multi-scale-model populus-trichocarpa svr support-vector-regression xgboost xgbregressor knn-regression gridsearchcv kfold-cross-validation shap shapley-values shapley-additive-explanations interpretable-machine-learning r2-score thesis
Language:Jupyter Notebook 1
kingjosephm / gb_vehicle_injuries
Coding challenge for a job interview examining the predictors of vehicle accident severity using GB Road Safety Data
lgbm light-gradient-boosting-machine machine-learning optuna python3 shapley-additive-explanations
Language:Jupyter Notebook 1
knmlprz / ShapEmotionsCorrectionFrontend
Frontend for ShapEmotionsCorrectionAPI
data-science data-visualization javascript nlp shapley-additive-explanations shapley-value
Language:JavaScript 1
savinims / TreeSHAP-Gassmann
Understanding the limitations of Gassmann's fluid substitution model using explainable ML
explainable-ai gassmann machinelearning random-forest regression-models rockphysics shapley-additive-explanations
Language:Python 1
Sebastian1981 / CustomerAnalytics_CreditDefaultPrediction
credit default prediction app
machine-learning shapley-additive-explanations webapp
Language:Jupyter Notebook 1
Sebastian1981 / Sales_Prediction
Use machine learning to find out what drives sales and predict sales
deep-neural-networks regression-analysis shapley-additive-explanations
Language:Jupyter Notebook 1
skswar / Explainable_ML_for_Production_Downtime_Shap
👨‍💻 This repository shows how machine learning and SHAP can be leveraged to understand the reasons of production downtime ⌛
catboost explainable-ml machine-learning shap shapley-additive-explanations tree-based-models
Language:Jupyter Notebook 1
grigoryangayane / Tranportation_XAI
AI applications can be found in various real-world systems, including vehicle system design and real-time car accident prediction. There is an increasing need to better explain AI-driven processes, especially in terms of potential legal disputes that might result from AI decisions. This analysis addresses this explainability and legal issues.
legaltech shapley-additive-explanations shapley-value transportation xai
Language:Jupyter Notebook 0
razamehar / Predicting-Bank-Customer-Churn
This project aims to predict bank customer churn using a dataset derived from the Bank Customer Churn Prediction dataset available on Kaggle. The dataset for this competition has been generated from a deep learning model trained on the original dataset, with feature distributions being similar but not identical to the original data.
adaboost-classifier classifier-model customer-churn-analysis customer-churn-prediction extra-trees-classifier gradient-boosting-classifier hyperparameter-tuning knn-classification python random-forest-classifier shapley-additive-explanations smote-sampling xgboost-classifier
Language:Jupyter Notebook 0
witrioktafiani / Sentiment-Analysis-Skincare
sentiment-analysis shapley-additive-explanations skincare vader-sentiment-analysis
Language:Jupyter Notebook 0
Atharva309 / XAI_diabetes
Explores diabetes prediction using various ML models and XAI techniques (SHAP, LIME, ALE) on the Pima Indian Diabetes dataset.
explainable-ai machine-learning accumulated-local-effects cnn mlp rnn shapley-additive-explanations local-interpretable-model-agnostic-explanations
Language:Jupyter Notebook
Kaushikjas10 / Liquefaction-gravel-eml-2023
This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of gravelly soils. This model is developed using LightGBM and SHAP.
earthquake earthquake-engineering geotechnical-engineering gravel liquefaction machine-learning artficial-intelligence explainable-artificial-intelligence explainable-machine-learning lightgbm lightgbm-classifier shap shapley-additive-explanations automl flaml shear-waves geotechnics
Language:Jupyter Notebook
tysonjohn015 / P04_AI_Credit_Risk_Model
Predict probability of default on credit
imbalanced-data logisticregression random-forest shapley-additive-explanations
Language:Jupyter Notebook

shapley-additive-explanations

AstraZeneca / awesome-shapley-value

haghish / shapley

balajissp / dash-shap-components

anniebritton / Ecological-Drought-ML-Modeling

g-aditi / customer-personality-analysis

LamineTourelab / Explainable-AI

upunaprosk / la-tda

jwuphysics / gnn-linking-lengths

carlacodes / boostmodels

josedv82 / NBA_Schedule_XGBoost_Classifier

Kaushikjas10 / Liquefaction-XGBoost-SHAP-Jas-Dodagoudar

LightnessOfBeing / ImpreciseSHAP

sachin17git / Malware-detection-ML

Akash-07DL / XGB_SHAP_Explainable_AI-Census_income_dataset-

anishjohnson / Credit_Card_Default_Prediction

iamlmn / simpleML

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

wideraHannes / SHAP-In-NLP

yhe-rs / hac-rf-shap

arpansharma-11 / XAI_breastCancer

arpansharma-11 / XAI_heartDataset

datatrigger / interpretable_machine_learning

DQ4781 / LF-ShapAnalysis

himasai97 / ML_Approaches

kingjosephm / gb_vehicle_injuries

knmlprz / ShapEmotionsCorrectionFrontend

savinims / TreeSHAP-Gassmann

Sebastian1981 / CustomerAnalytics_CreditDefaultPrediction

Sebastian1981 / Sales_Prediction

skswar / Explainable_ML_for_Production_Downtime_Shap

grigoryangayane / Tranportation_XAI

razamehar / Predicting-Bank-Customer-Churn

witrioktafiani / Sentiment-Analysis-Skincare

Atharva309 / XAI_diabetes

Kaushikjas10 / Liquefaction-gravel-eml-2023

tysonjohn015 / P04_AI_Credit_Risk_Model