Popular GitHub Repositories for Aix

FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

Aix-bench, the Java benchmark for code synthesis problem.

Use machine learning in AppInventor, with easy training using text, images, or numbers through the Machine Learning for Kids website.

AiX3 (artificial intelligence X3 network), aggregating more than 760 artificial intelligence platforms around the world, allows you to use the power of AI. AiX3(人工智能X3网),聚合全球760多个人工智能平台，让你尽情使用Ai的力量。

This notebook is ispired by the AIX360 HELOC Credit Approval Tutorial, which shows different explainability methods for a credit approval process. Here XGBoost is used for classification, achieving better accuracy than most of the models used in that notebook. Then, feature importance methods are shown, to be compared with the Data Scientist explanations methods provided in the above notebook. The first ones come directly with XGBoost and the other is based on SHAP.

Imagine boarding the Titanic in 2021, and you have provided all your details as a passenger to the captain. There is are three people involved, the data scientist, captain and the passenger. Imagine the company who has built Titanic has created a machine ML model to predict the rate of survival of the passengers, in case of a disaster. The job of the data scientist is to make a model that is explainable to the passengers who are not technical, and that they get the answer about the reasons why they may not survive.