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Name: heyuan
Type: User
Name: heyuan
Type: User
Introduction to explaining data and machine learning models with aif360
Regression model that predicts demand for bicycle rentals
Catboost model implementation(SHAP)
Model that can predict chances of college admission, using a pipeline for the different feature types, compared Ridge and LGBMRegressor pipeline with hyper-parameter tuning and train/test scores.
This application is based on a CatBoost machine learning model. This basically takes four queries from the user (Upazila/Thana name, Network availability (3G/4G), District, and Zip code) and outputs the best operator for that location. This model was trained on the data I collected from Opensingnal application. I collected 22,360 data for 559 locations of Bangladesh. Currently this model is based on a static dataset but in the future, I have a plan to upgrade it to a real-time data collection-based model.
A library for debugging/inspecting machine learning classifiers and explaining their predictions
Examples of how to do feature engineering and Xgboost parameter tuning
python风控建模实战lendingClub
Explainable Machine Learning using SHAP for the CAC Consortium
Machine Learning Interpretability - LIME e SHAP
Developing an interpretable machine learning model for predicting the shear strength of RC squat walls using XGBoost and SHAP.
A simple implementation to regression problems using Python 2.7 and LightGBM
Using Lime and Shap modules to understand how black box models works.
A simple data science project that involves web scraping, data cleaning and visualization, model building, and model explanation using character data from Marvel Fandom.
CCCF-数据挖掘-企业非法集资风险预测
A GBDT NumerAI ensemble model using LightGBM + CatBoost with KFold CV.
Comparative Analysis of Machine Learning Approaches on the Prediction of the Electronic Properties of Perovskite: A Case Study of the ABX3 and A2BB’X6
Goal is to predict the concrete compressive strength using collected data
Interpretation results of machine learning models by SHAP
A game theoretic approach to explain the output of any machine learning model.
Code and documentation for experiments in the TreeExplainer paper
Useful scripts for VASP(very useful)
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.