{"repo":"AmirhosseinHonardoust/Shap-Mini","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/Shap-Mini","clone":"git clone https://github.com/AmirhosseinHonardoust/Shap-Mini.git","description":"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.","language":"Python","stars":20,"topics":["classification-model","data-science","data-visualization","explainable-ai","feature-importance","interpretable-machine-learning","logistic-regression","machine-learning","model-audit","model-explainability"],"license":"MIT","category":"machine-learning","readme_excerpt":"SHAP Mini: Explainable AI for Tabular Models SHAP Mini is a lightweight and reproducible project that demonstrates model explainability using the SHAP (SHapley Additive exPlanations) framework. It helps visualize how individual features contribute to model predictions in simple tabular machine learning problems. The project is intentionally minimal, using only RandomForest and LogisticRegression , so users can easily inspect, understand, and visualize how SHAP values reveal the inner workings of black-box models. --- Folder Structure --- Key Features - Automatic data generation if no dataset is provided. - Two baseline models: - RandomForestClassifier ( rf ) - LogisticRegression ( logreg ) - SHAP visualization suite: - Global feature importance bar plot - Dependence plots for any feature - JSON-based outputs for reproducibility - Works on CPU-only machines and installs in Source: outputs/metrics.json (synthetic dataset with 2000 samples, 12 features). This confirms the RandomForest learned a strong signal, mainly dominated by two features ( feature 3 and feature 8 ). --- Theoretical Background SHAP (SHapley Additive Explanations) assigns each feature an importance value for a particular prediction. It is based on the concept of Shapley values from cooperative game theory. For each prediction: Every feature is treated as a player in a coalition game. The SHAP value represents the average marginal contribution of that feature to the model output across all possible feature subs","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/Shap-Mini/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}