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. 📊📈📉👨‍💻

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

Visualize a Decision Tree using dtreeviz

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. 📉👨‍💻📊📈

ML project focused on predicting Titanic passenger survival using various algorithms and extensive data analysis techniques. This project includes detailed data visualization and interpretation to uncover key factors affecting survival. By leveraging various ML models the analysis aims to achieve high predictive accuracy.

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

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.

API backend to deploy a machine learning model to the web