model-interpretation

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: An interpretable hybrid framework for accurate peptide toxicity prediction fusing protein language model-based embeddings with conventional descriptors"

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

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

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

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

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 contains the LifeExpectancy Prediction Project, a comprehensive data science project aimed at predicting life expectancy based on various health, economic, and social factors. The project includes steps for data preprocessing, exploratory data analysis (EDA), model selection, training, hyperparameter tuning, and model interpretation