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?

Overview of machine learning interpretation techniques and their implementations

Model Interpretability via Hierarchical Feature Perturbation

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

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. 📊📈📉👨‍💻

Integrating multimodal data through heterogeneous ensembles

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

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

Visualize a Decision Tree using dtreeviz

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

API backend to deploy a machine learning model to the web

This project included a XGBoost Regression model, which predict the purchase possibility of a customer customer based on their online shopping behavior. In addtion, a recommendation model including both CF and CBF was built using customer purchase transaction data.

Exploratory data analysis, data modelling, model building and interpretation, machine learning production, quality assurance