Projects

explainy is a library for generating machine learning models explanations in Python. It uses methods from Machine Learning Explainability and provides a standardized API to create feature importance explanations for samples. The API is inspired by scikit-learn and has three core methods. explainy comes with four different algorithms to create either global or local and contrastive or non-contrastive model explanations.

Technologies: Python, Machine Learning Explainability, scikit-learn, SHAP, Python Packaging, GitHub Actions

Repository: explainy

Docs: https://explainy.readthedocs.io/en/latest/

This repository contains a TicTacToe-Environment based on the OpenAI Gym module. TicTacToe is a board game, where two players compete to place three stones of their color in parallel (horizontally or vertically) or diagonally to win the game.

Technologies: Python, Reinforcement Learning, OpenAI Gym, Q-Learning

Repository: OpenAI-Gym-TicTacToe-Environment

This repository contains a new method to measure the word ambiguity in legal corpora, based on a word2vec model. The code has been developed to measure the word ambiguity in the written text of opinions by the U.S. Supreme Court and the German Bundesgerichtshof, which are representative courts of the common-law and civil-law court systems.

Technologies: Python, NLP, Text Embeddings, Gensim

Repository: legal-entropy