shapley-additive-explanations

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

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upunaprosk / la-tda

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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

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anniebritton / Ecological-Drought-ML-Modeling

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📊🛰️ 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

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Kaushikjas10 / Liquefaction-XGBoost-SHAP-Jas-Dodagoudar

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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

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wideraHannes / SHAP-In-NLP

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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

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👁 NBA_Schedule_XGBoost_Classifier

josedv82 / NBA_Schedule_XGBoost_Classifier

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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

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yhe-rs / hac-rf-shap

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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

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jwuphysics / gnn-linking-lengths

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Measuring galaxy environmental distance scales with GNNs and explainable ML models

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

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carlacodes / boostmodels

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gradient-boosted regression and decision tree models on behavioural animal data (PLOS Computational Biology, doi: https://doi.org/10.1371/journal.pcbi.1011985)

python behavior machine-learning sklearn regression neuroscience pandas lightgbm statsmodels shap shapley-additive-explanations

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Python

datatrigger / interpretable_machine_learning

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Getting explanations for predictions made by black box models.

lime interpretability black-box-model feature-importance interpretable-ml surrogate-models interpretable-machine-learning shap explainability explainable-machine-learning shapley-additive-explanations

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LightnessOfBeing / ImpreciseSHAP

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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