Search Results for "outlier"
Sort By:
Showing 37 open source projects for "outlier"
View related business solutions-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
1
Python Outlier Detection
A Python toolbox for scalable outlier detection
PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as outlier detection or anomaly detection. PyOD includes more than 30 detection algorithms, from classical LOF (SIGMOD 2000) to the latest COPOD (ICDM 2020) and SUOD (MLSys 2021). Since 2017, PyOD [AZNL19] has been successfully used in numerous academic researches and commercial products [AZHC+21, AZNHL19]. PyOD has multiple neural network-based models, e.g., AutoEncoders, which are implemented in both PyTorch and Tensorflow. ... -
2
Alibi Detect
Algorithms for outlier, adversarial and drift detection
Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. Both TensorFlow and PyTorch backends are supported for drift detection. -
3
BenchmarkDotNet
Powerful .NET library for benchmarking
BenchmarkDotNet is a powerful .NET library designed for creating accurate and reproducible benchmarks. It handles complexities like warm-up, outlier removal, and statistical analysis, presenting results in a clean, customizable summary format. BenchmarkDotNet has tons of features that are essential in comprehensive performance investigations. Four aspects define the design of these features: simplicity, automation, reliability, and friendliness. A lot of hand-written benchmarks produce wrong numbers that lead to incorrect business decisions. ... -
4
HDBSCAN
A high performance implementation of HDBSCAN clustering
HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster... -
Gemini 3 and 200+ AI Models on One PlatformBuild, govern, and optimize agents and models with Gemini Enterprise Agent Platform.
-
5
CapFrameX
Frametime capture and analysis tool
...Importantly, the tool also integrates with sensor inputs (CPU, GPU, VRAM, temps, etc.) and overlays statistics in-game via Rivatuner Statistics Server, so you get in-situ feedback while you run. For benchmarking, it supports aggregation, filtering, outlier detection, and export of records to CSV/Excel for further analysis or reporting. The project is suited for reviewers, hardware testers, and power users who want to dig deeper than simple FPS numbers and want to diagnose performance issues. -
6
Awesome production machine learning
Curated list of awesome open source libraries
This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning. Open-source frameworks, tutorials, and articles curated by machine learning professionals. Open-source bias audit toolkits for data scientists, machine learning researchers, and policymakers to audit machine learning models for discrimination and bias, and to make informed and equitable decisions around developing and... -
7
Anomaly Detection Learning Resources
Anomaly detection related books, papers, videos, and toolboxes
Anomaly Detection Learning Resources is a curated open-source repository that collects educational materials, tools, and academic references related to anomaly detection and outlier analysis in data science. The project serves as a centralized index for researchers and practitioners who want to explore algorithms, datasets, and publications associated with detecting unusual patterns in data. The repository organizes resources into structured categories such as books, tutorials, academic papers, datasets, benchmark frameworks, and open-source toolkits. ... -
8
hyperfine
A command-line benchmarking tool
...Constant feedback about the benchmark progress and current estimates. Warmup runs can be executed before the actual benchmark. Cache-clearing commands can be set up before each timing run. Statistical outlier detection to detect interference from other programs and caching effects. Export results to various formats: CSV, JSON, Markdown, AsciiDoc. Parameterized benchmarks (e.g. vary the number of threads). Cross-platform. Hyperfine will automatically determine the number of runs to perform for each command. By default, it will perform at least 10 benchmarking runs and measure for at least 3 seconds. ... -
9
Cleanlab
The standard data-centric AI package for data quality and ML
cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset. To facilitate machine learning with messy, real-world data, this data-centric AI package uses your existing models to estimate dataset problems that can be fixed to train even better models. cleanlab cleans your data's labels via state-of-the-art confident learning algorithms, published in this paper and blog. See some of the datasets cleaned with cleanlab at labelerrors.com. This package helps you... -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
-
10
unwarp
Increase image resolution by eliminating atmospheric distortion
...The core technique matches features between images, applies triangulation across the entire frame, and warps each pixel to its optimal position. The resulting aligned images are then averaged into a single output, with automatic outlier elimination for cleaner results. Unwarp is equally effective for telephoto image series of terrestrial objects affected by heat shimmer or atmospheric distortion. Unwarp supports common image formats and provides an intuitive interface for both beginners and advanced users. The project is currently in early development, with active work on improving algorithm performance and expanding feature sets. -
11
Crypto-Pump-Bot-Ai-Powered
A cutting-edge AI-driven cryptocurrency trading bot designed to detect
A cutting-edge AI-driven cryptocurrency trading bot designed to detect and respond to market pump-and-dump activities. This bot leverages advanced machine learning models and sophisticated analysis techniques to identify opportunities, analyze market trends, and execute trades with precision. -
12
MOA - Massive Online Analysis
Big Data Stream Analytics Framework.
A framework for learning from a continuous supply of examples, a data stream. Includes classification, regression, clustering, outlier detection and recommender systems. Related to the WEKA project, also written in Java, while scaling to adaptive large scale machine learning. -
13
Data Preprocessing Automate
Data Preprocessing Automation: A GUI for easy data cleaning & visualiz
Data Preprocessing Automation is a Python-based GUI application designed to simplify and automate data preprocessing tasks. It allows users to upload Excel files, automatically handle missing values, remove duplicates, and detect and remove outliers using statistical methods. The application provides data visualization tools, including box plots for distribution analysis and scatter plots for exploring relationships between variables. Users can download the processed data for further... -
14
Feature-engine
Feature engineering package with sklearn like functionality
Feature-engine is a Python library with multiple transformers to engineer and select features for use in machine learning models. Feature-engine's transformers follow Scikit-learn's functionality with fit() and transform() methods to learn the transforming parameters from the data and then transform it. -
15
HiPlot
HiPlot makes understanding high dimensional data easy
...Because it renders as self-contained HTML, you can embed the visualization in notebooks, export it, or serve it as a lightweight web app for teammates. HiPlot also offers summary statistics, correlation hints, and outlier highlighting to surface patterns that aren’t obvious from raw tables. -
16
photo-z CO binary classifier
A binary classifier neural network to identify cat. outier photo-zs
This strategy of binary classification to identify catastrophic outliers is presented in “Machine Learning Classification to Identify Catastrophic Outlier Photometric Redshift Estimates.” J. Singal, G. Silverman, E. Jones, T. Do, B. Boscoe, and Y. Wan, 2022, Astrophysical Journal, 928, 6 This package contains Jupyter notebooks and supporting files which do the following: - Perform a neural network regression to estimate photo-zs (photoz_regression_mlp.ipynb) - Take a data set with estimated photo-zs, set aside 30% of the galaxies as a base evaluation set, and output training sets for a binary classifier with varying portions of catastrophic outliers using the remaining 70% of the galaxies (process_data_for_binary_classifier.ipynb) - Perform a neural network binary classification to determine catastrophic outliers given a data set with photometry and estimated photo-zs (catastrophic_outlier_binary_classification.ipynb) Supporting files: galaxy_utils.py models.py -
17
Reliable Metrics for Generative Models
Code base for the precision, recall, density, and coverage metrics
Reliable Fidelity and Diversity Metrics for Generative Models (ICML 2020). Devising indicative evaluation metrics for the image generation task remains an open problem. The most widely used metric for measuring the similarity between real and generated images has been the Fréchet Inception Distance (FID) score. Because it does not differentiate the fidelity and diversity aspects of the generated images, recent papers have introduced variants of precision and recall metrics to diagnose those... -
18
awesome-TS-anomaly-detection
List of tools & datasets for anomaly detection on time-series data
All lists are in alphabetical order. In the lists, maintained projects are prioritized vs not mantained. A repository is considered "not maintained" if the latest commit is > 1 year old, or explicitly mentioned by the authors. -
19
MoPAC
The Modular Pipeline for the Analysis of CRISPR screens
To facilitate the comparison of gene essentialities in two or more cell samples, we propose MoPAC (Modular Pipeline for Analysis of CRISPR screens), a Shiny-driven interactive tool for differential essentiality analysis in CRISPR/Cas9 screens. For installation and usage instructions please refer to the wiki page. -
20
Skalli
IVS & ILRS Reference Point Determination
Skalli is a simple tool to estimate the IVS (International VLBI Service for Geodesy and Astrometry) and ILRS (International Laser Ranging Service) reference point of a radio or laser telescope. -
21
JPIV
Particle Image Velocimetry
JPIV is a platform independent, graphical stand-alone application for Particle Image Velocimetry (PIV) written in Java. PIV is an optical technique for measuring fluid flow velocities. JPIV moved to GitHub. Please visit us at: https://eguvep.github.io/jpiv/ -
22
L1-norm-robust-regression
linear regression on the basis of minimal absolute deviations error
...The "standard" linear regression minimizes the squared error. (L2-norm-regression). It allows for a simple solution process, hence its popularity. But it is not robust to outlier points. L1-norm regression is robust with respect to outliers but the solution algorithm is more difficult. It minimizes the sum of absolute deviations. -
23
UMAD
Universal Management and Analysis of Data
The project mainly includes three parts:similarity searching,classfication and outlier detection. All those three methods are based on data items in metric space, which contains complex objects like picture,video,DNA,protein and so on, it will consumes large amount of cpu time to calculate out the distance between any two complex objects shown before.Our methods in the project reduces the times of distance calculation and improves the effectiveness of data retrieval. -
24
ilodids
Incremental and local outlier detection
-
25
NOFI ranking
The Non-Outlier Fragment Ion ranking for enhaced DIA quantification
...The outline is as follows: 1) The input contains the list of SWATH fragment ion XICs from the identified and quantified peptides by software tools such as Skyline and OpenSWATH. 2) The first step in NOFI is the computation of the 4 attributes (RTd, FWHMd, IRd and IRrep) used to represent each fragment ion as a vector. 3) Multivariate outlier detection techniques are used to rank all the fragment ions from each peptide. 4) Several figures are generated (a pdf file) to visualize the effect of the Top-N fragment ions over different indicators. 5) The user can choose the number of top fragment ions per peptide, thereby utilizing the optimal subset of high priority Top-N NOFIs for quantification while excluding the impaired fragment ions.
