Stars
A curated list of awesome responsible machine learning resources.
Main web site for Statistical Thinking for the 21st Century
Statistical Rethinking course winter 2022
A Spotify player in the terminal with full feature parity
A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
PyMC educational resources
Interpretability and explainability of data and machine learning models
A human-readable regular expression module for Python.
A library of extension and helper modules for Python's data analysis and machine learning libraries.
The Apache Flink SQL Cookbook is a curated collection of examples, patterns, and use cases of Apache Flink SQL. Many of the recipes are completely self-contained and can be run in Ververica Platfor…
Rapid fuzzy string matching in Python using various string metrics
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Beyond Accuracy: Behavioral Testing of NLP models with CheckList
Natural Gradient Boosting for Probabilistic Prediction
A Python scikit for building and analyzing recommender systems
A Python implementation of LightFM, a hybrid recommendation algorithm.
ml-tue / automated-string-cleaning
Forked from Johnnynater/thesisRepository for my master thesis on automated string handling
Improving XGBoost survival analysis with embeddings and debiased estimators
An implementation of the gradient boosted tree distance presented in "Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable". S Tan, M Soloviev, G Hooker, and MT Wells. ACM-IMS…
Repository of the paper "Defining Locality for Surrogates in Post-hoc Interpretablity" published at 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018)
A simple and efficient tool to parallelize Pandas operations on all available CPUs
SQL databases in Python, designed for simplicity, compatibility, and robustness.
This curated list contains python packages for time series analysis
An educational resource to help anyone learn deep reinforcement learning.
A light-weight, flexible, and expressive statistical data testing library
Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadri. Collaborators welcome.
For calculating global feature importance using Shapley values.