A python library for decision tree visualization and model interpretation.

InterpretDL: Interpretation of Deep Learning Models，基于『飞桨』的模型可解释性算法库。

A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.

Overview of different model interpretability libraries.

A set of tools for leveraging pre-trained embeddings, active learning and model explainability for effecient document classification

FastAI Model Interpretation with LIME

What Has Been Enhanced in my Knowledge-Enhanced Language Model?

Official implementation of HyPepTox-Fuse framework

Overview of machine learning interpretation techniques and their implementations

Integrating multimodal data through heterogeneous ensembles

Implémentation d'un modèle de scoring (OpenClassrooms | Data Scientist | Projet 7)

Model Interpretability via Hierarchical Feature Perturbation

This repository has all of the assignments I had to do for the Standard Bank Data Science Virtual Experience Program. 📉👨‍💻📊📈

The tasks I was required to complete as a part of the BCG Open-Access Data Science & Advanced Analytics Virtual Experience Program are all contained in this repository. 📊📈📉👨‍💻

Using LIME and SHAP for model interpretability of Machine Learning Black-box models.

Advise one of Cognizant’s clients on a supply chain issue by applying knowledge of machine learning models.

Visualize a Decision Tree using dtreeviz

Analyzed customer churn using transaction data. Built ML model to predict lapses. Dataset includes customer status, collection/redemption info, and program tenure. Delivered business presentation outlining modeling approach, findings, and churn reduction strategies.

This repository provides a practical, data-centric AI/ML module for biomedical researchers. It covers R programming, data preparation, model building, and AI/ML applications using AWS SageMaker and Jupyter notebooks.