A python library for decision tree visualization and model interpretation.
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Updated
Aug 29, 2024 - Jupyter Notebook
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.
Streamlit dashboard frontend (user interface) to deploy a machine learning model to the web
API backend to deploy a machine learning model to the web
Exploratory data analysis, data modelling, model building and interpretation, machine learning production, quality assurance
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