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A new framework to generate interpretable classification rules
Parse and Process AWS IAM Policies, Statements, ARNs, and wildcards.
Copy of https://github.com/Nordstrom/hello-retail
📝 An awesome Data Science repository to learn and apply for real world problems.
A collection of research papers on decision, classification and regression trees with implementations.
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
Repository for the Tetrad Project, www.phil.cmu.edu/tetrad.
Interpretability and explainability of data and machine learning models
Working repository for Causal Tree and extensions
This repository contains the source code of the paper "Learning Accurate and Interpretable Decision Rule Sets from Neural Networks".
An implementation for Time changing decision tree
Uplift modeling and causal inference with machine learning algorithms
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphic…
An index of algorithms for learning causality with data
Rust制作的BiliBili漫画下载器：无环境依赖，高性能，支持导出pdf、epub、zip
Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data
A collections of public and free annotated datasets of relationships between entities/nominals (Portuguese and English)
A large collection of system log datasets for AI-driven log analytics [ISSRE'23]
A machine learning toolkit for log-based anomaly detection [ISSRE'16]
APT & CyberCriminal Campaign Collection