shap-values

Here are 44 public repositories matching this topic...

oegedijk / explainerdashboard

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

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Python

archd3sai / Customer-Survival-Analysis-and-Churn-Prediction

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In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.

random-forest hazard flask-application data-analysis survival-analysis partial-dependence-plot explainable-ai customer-churn-prediction shap-values customer-survival-analysis

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Jupyter Notebook

oegedijk / explainingtitanic

Sponsor

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A demonstration of the explainerdashboard package that that displays model quality, permutation importances, SHAP values and interactions, and individual trees for sklearn RandomForestClassifiers, etc

xai explainable shap-values permutation-importances individual-trees explainerdashboard

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Python

AmirhosseinHonardoust / Shap-Mini

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A minimal, reproducible explainable-AI demo using SHAP values on tabular data. Trains RandomForest or LogisticRegression models, computes global and local feature importances, and visualizes results through summary and dependence plots, all in under 100 lines of Python.

python open-source data-science machine-learning random-forest tabular-data data-visualization logistic-regression reproducibility model-interpretation feature-importance classification-model explainable-ai interpretable-machine-learning shap responsible-ai shap-values model-explainability trustworthy-ai model-audit

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Python

haghish / shapley

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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

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adiag321 / CRM-Analysis-for-Marketing-data

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In this project, we have to create a predictive model which allows the company to maximize the profit of the next marketing campaign

machine-learning statistics data-analysis hyperparameter-tuning classification-algorithm feature-importance gridsearchcv crm-analysis shap-values marketing-analysis

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Jupyter Notebook

LGDiMaggio / Explainable-AI-for-Machine-Fault-Diagnosis

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This project uses Explainable AI (XAI) to interpret machine learning models for diagnosing faults in industrial bearings. By applying SVM and kNN models and leveraging SHAP values, it enhances the transparency and reliability of machine learning in industrial condition monitoring.

svm knn shapely condition-monitoring explainable-ai bearing-fault-diagnosis rotating-machinery-fault-diagnosis shap-values

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Jupyter Notebook

Arsu-Lab / Different-Algorithms-Uncover-Different-Patterns-BrainAge-Prediction

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Github Repository for the paper "Different Algorithms (Might) Uncover Different Patterns: A Brain-Age Prediction Case Study" - BIBM 2023

eeg eeg-classification brain-age brain-age-prediction shap-values

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Jupyter Notebook

CSFelix / Data-Science-Mental-Maps

Sponsor

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🐍 Mental Maps Related to Contents in Data Science 🐍

python data-science machine-learning computer-vision deep-learning scale encoder data-transformation cross-validation pipelines xgboost imputation standardization pickle feature-engineering data-preparation normalization ordinal-encoder shap-values one-hot-encoder

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fdhot / app

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Framework de Mixture of Experts para Explicabilidade de Estados de Ansiedade

data-visualization causal-inference ddqn mixture-of-experts explainability shap-values llms-reasoning anxiety-states regression-based-causal-discovery

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Jupyter Notebook

guanharry / CD-Tron

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A lightweight clinical large language model (LLM) for cognitive decline detection from clinical notes. The project includes synthetic EHR data for rapid validation and experimentation. The paper was published in Journal of Biomedical Informatics: CD-Tron: Leveraging Large Clinical Language Models for Early Detection of Cognitive Decline from EHR

ehr clinical-notes alzheimers-disease interpretability synthetic-data unstructured-data interpretable-ai alzheimer-disease-prediction shap clinical-nlp ehr-data shap-values large-language-models llm cognitive-decline llms medical-llm clinical-llm lightweight-llm

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Python

Nirmala-research / YOLOv7-XAI

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Multiclass Skin lesion localization and Detection with YOLOv7-XAI Framework with explainable AI

image-processing skin-detection explainable-artificial-intelligence skin-cancer explainable-ai early-detection multiclass-image-classification melanoma-detection shap-values melanoma-classification lime-explanation skin-cancer-diagnosis image-processing-yolo yolo-object-detection-new-with-opencv yolo-object-detection-recent-with-opencv yolov7-xai-2025-object-detection-with-opencv yolo-recent-with-opencv yolo-2025-new-version-latest

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Python

dphot / app

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Análise Avançada de Intervenção para Ansiedade com SHAP

data-visualization machine-learning-pipelines feature-importance explainability shap-values llms-reasoning anxiety-intervention

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Jupyter Notebook

lucasthim / covid19-prediction

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Prediction if patients with symptoms have COVID-19 based on clinical variables (blood related variables, urine related variables, age, etc)

shap-values visualize-feature-weights clinical-variables

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Jupyter Notebook

kochlisGit / Wine-Preference-Analysis

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The purpose of this work is the modeling of the wine preferences by physicochemical properties. Such model is useful to support the oenologist wine tasting evaluations, improve and speed-up the wine production. A Neural Network was trained using Tensorflow, which was later tuned in order to achieve high-accuracy quality predictions.