Philippe-Neveux

Philippe-Neveux's Stars

• scikit-learn-contrib/MAPIE

  A scikit-learn-compatible module to estimate prediction intervals and control risks based on conformal predictions.

  Language:Jupyter Notebook1.3k111

• scikit-learn-contrib/qolmat

  A scikit-learn-compatible module for comparing imputation methods.

  Language:Python1352

• unionai-oss/pandera

  A light-weight, flexible, and expressive statistical data testing library

  Language:Python3.4k311

• SelfExplainML/PiML-Toolbox

  PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics

  Language:Jupyter Notebook1.2k123

• beroguedou/basmatinet

  End-to-end machine learning project for rices detection

  Language:Python461

• sebp/scikit-survival

  Survival analysis built on top of scikit-learn

  Language:Python1.1k216

• interpretml/interpret

  Fit interpretable models. Explain blackbox machine learning.

  Language:C++6.3k734

• PythonOT/POT

  POT : Python Optimal Transport

  Language:Python2.4k502

• EFS-OpenSource/calibration-framework

  The net:cal calibration framework is a Python 3 library for measuring and mitigating miscalibration of uncertainty estimates, e.g., by a neural network.

  Language:Python34742

• JMLToulouse/Fair-ML-4-Ethical-AI

  Fair Statistical Learning Algorithms for Ethical Artificial Intelligence

  Language:Jupyter Notebook2

• csinva/imodels

  Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

  Language:Jupyter Notebook1.4k124

• parrt/dtreeviz

  A python library for decision tree visualization and model interpretation.

  Language:Jupyter Notebook3k334

• iancovert/shapley-regression

  For calculating Shapley values via linear regression.

  Language:Python6613

• XAI-ANITI/ethik

  :mag_right: A toolbox for fair and explainable machine learning

  Language:Python535

• salimamoukou/acv00

  ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any model or data and different Shapley Values for tree-based models.

  Language:Jupyter Notebook10011

• dswah/pyGAM

  [HELP REQUESTED] Generalized Additive Models in Python

  Language:Python876160

• scikit-learn-contrib/skope-rules

  machine learning with logical rules in Python

  Language:Jupyter Notebook62598

• great-expectations/great_expectations

  Always know what to expect from your data.

  Language:Python10k1.5k

• pythonprofilers/memory_profiler

  Monitor Memory usage of Python code

  Language:Python4.4k380

• scikit-learn/scikit-learn

  scikit-learn: machine learning in Python

  Language:Python60.3k25.4k

• LabeliaLabs/referentiel-evaluation-dsrc

  Référentiel d'évaluation data science responsable et de confiance

  718

• pandas-dev/pandas

  Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

  Language:Python43.9k18k

• interpretml/DiCE

  Generate Diverse Counterfactual Explanations for any machine learning model.

  Language:Python1.4k188

• ml-tooling/best-of-ml-python

  🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

  18.2k2.5k

• MAIF/shapash

  🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

  Language:Jupyter Notebook2.7k334

• shap/shap

  A game theoretic approach to explain the output of any machine learning model.

  Language:Jupyter Notebook23k3.3k

• Trusted-AI/AIF360

  A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

  Language:Python2.5k840

• fairlearn/fairlearn

  A Python package to assess and improve fairness of machine learning models.

  Language:Python2k420

• Trusted-AI/adversarial-robustness-toolbox

  Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

  Language:Python4.9k1.2k

• wikistat/Fair-ML-4-Ethical-AI

  Fair Statistical Learning Algorithms for Ethical Artificial Intelligence

  Language:Jupyter Notebook2418