Popular GitHub Repositories for Outlier Treatment

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques

Anomaly detection related books, papers, videos, and toolboxes

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 graphical models and potential outcomes frameworks.

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

List of tools & datasets for anomaly detection on time-series data.

Algorithms for outlier, adversarial and drift detection

TODS: An Automated Time-series Outlier Detection System

:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps

A Python Library for Graph Outlier Detection (Anomaly Detection)

Benchmarking Generalized Out-of-Distribution Detection

Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022.

Out-of-distribution detection, robustness, and generalization resources. The repository contains a curated list of papers, tutorials, books, videos, articles and open-source libraries etc

Luminaire is a python package that provides ML driven solutions for monitoring time series data.

ML powered analytics engine for outlier detection and root cause analysis.

A Deep Graph-based Toolbox for Fraud Detection

Curated list of open source tooling for data-centric AI on unstructured data.

MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.

Deep Anomaly Detection with Outlier Exposure (ICLR 2019)

A collection of Bayesian data analysis recipes using PyMC3