Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
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Updated
Jul 27, 2024 - Python
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
Fast SHAP value computation for interpreting tree-based models
利用lightgbm做(learning to rank)排序学习,包括数据处理、模型训练、模型决策可视化、模型可解释性以及预测等。Use LightGBM to learn ranking, including data processing, model training, model decision visualization, model interpretability and prediction, etc.
A power-full Shapley feature selection method.
Validation (like Recursive Feature Elimination for SHAP) of (multiclass) classifiers & regressors and data used to develop them.
streamlit-shap provides a wrapper to display SHAP plots in Streamlit.
Real-time explainable machine learning for business optimisation
Local explanations with uncertainty 💐!
A multivariate multi-step LSTM forecasting model for tuberculosis incidence with model explanation
🏆데이콘 AI해커톤 대회 우수상 솔루션🏆
Enabling interactive plotting of the visualizations from the SHAP project.
Here, we use Deep SHAP (or SHAP) to explain the behavior of nanophotonic structures learned by a convolutional neural network (CNN). Reference: https://pubs.acs.org/doi/full/10.1021/acsphotonics.0c01067
SHAP-Based Interpretable Object Detection Method for Satellite Imagery
A methodology designed to measure the contribution of the features to the predictive performance of any econometric or machine learning model.
Interpretable machine learning based on Shapley values
Machine Learning-based tool to assess the functional relevance of splice isoforms.
Pytorch Implementation of the Explainable Conditional Adversarial Autoencoder using Saliency Maps and SHAP (J. of Imaging - MDPI)
This project aims to build and compare four different models predicting the dropout rates in schools in New York state as well as to understand why models make a certain prediction (see PDF file with the memo for details)
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