Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

Diffusers-Interpret 🤗🧨🕵️‍♀️: Model explainability for 🤗 Diffusers. Get explanations for your generated images.

A python library to send data to Arize AI!

CrysXPP: An Explainable Property Predictor for Crystalline Materials (NPJ Computational Materials - 2022)

A curated list of awesome open source tools and commercial products for monitoring data quality, monitoring model performance, and profiling data 🚀

code for studying OpenAI's CLIP explainability

This project develops an ML binary classification model to predict phishing webpages.

Example projects for Arthur Model Monitoring Platform

Study on the performance of pre-trained models (VGG16, EfficientNetb0, ResNet50, ViT16) with weight fine tuning, as well as classical ML algorithms (Naive Bayes, Logistic Regression, Random Forest) on a dataset of 6.806 fungi microscopy Images utilizing Pytorch.

The official Python library for Openlayer, the Continuous Model Improvement Platform for AI. 📈

Capture fundamentals around ethics of AI, responsible AI from principle, process, standards, guidelines, ecosystem, regulation/risk standpoint.

Machine Learning Individual Project - November 23, 2021

The Fraud Detection project aims to improve identification of fraudulent activities in e-commerce and banking by developing advanced machine learning models that analyze transaction data, employ feature engineering, and implement real-time monitoring for high accuracy fraud detection.

A reusable codebase for fast data science and machine learning experimentation, integrating various open-source tools to support automatic EDA, ML models experimentation and tracking, model inference, model explainability, bias, and data drift analysis.

Writeup on classification model for predicting outcomes of NFL games, focusing on explainability. (+ project writeup)

Java client to interact with Arize API

Predict which powerlifters will have the highest one-rep-max deadlift

Machine Learning Final Project - December 04, 2021

Explaining Trees (LightGBM) with FastTreeShap (Shapley) and What if tool

This project provides a performance evaluation of credit card default prediction. Thus different models are used to test the variable in predicting the credit default and we found Random Forest Classifier performs the best with a recall of 0.95 on the test set.