Search Results for "regression"
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Showing 426 open source projects for "regression"
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Visual Regression Tracker
Backend and Frontend application for tracking differences via image
Open source, self-hosted solution for visual testing and managing results of visual testing. Service receives images, performs pixel-by-pixel comparisons with its previously accepted baseline, and provides immediate results in order to catch unexpected changes. Use implemented libraries to integrate with existing automated suites by adding assertions based on image comparison. We provide native integration with automation libraries, core SDK and Rest API interfaces that allow the system to... -
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Cypress Visual Regression
Module for adding visual regression testing to Cypress
Module for adding visual regression testing to Cypress. -
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React Native Owl
Visual regression testing library for React Native
React Native Owl is a visual regression testing library for React Native that enables developers to introduce visual regression tests to their apps for iOS and Android. Being heavily inspired by Detox, an end-to-end testing and automation framework, this library uses a similar API that makes setting up react-native-owl and running the tests locally and on your preferred CI service seamless. -
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MultivariateStats.jl
A Julia package for multivariate statistics and data analysis
A Julia package for multivariate statistics and data analysis (e.g. dimensionality reduction). -
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Machine Learning Octave
MatLab/Octave examples of popular machine learning algorithms
This repository contains MATLAB / Octave implementations of popular machine learning algorithms, along with explanatory code and mathematical derivations, intended as educational material rather than production code. Implementations of supervised learning algorithms (linear regression, logistic regression, neural nets). The author’s goal is to help users understand how each algorithm works “from scratch,” avoiding black-box library calls. Code written so as to expose and comment on mathematical steps. The repository includes clustering, regression, classification, neural networks, anomaly detection, and other standard ML topics. ... -
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SikuliX
SikuliX version 2.0.0+ (2019+)
SikuliX automates anything you see on the screen of your desktop computer running Windows, Mac or some Linux/Unix. It uses image recognition powered by OpenCV to identify GUI components and can act on them with mouse and keyboard actions. This is handy in cases when there is no easy access to a GUI's internals or the source code of the application or web page you want to act on. -
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Passmark
The open-source Playwright library for AI browser regression testing
The Passmark project is an open-source AI-powered regression testing framework built on top of Playwright that enables developers to write end-to-end browser tests using natural language instead of traditional scripting. It is designed to simplify and accelerate testing workflows by allowing AI models to interpret human-readable instructions and translate them into executable browser actions. -
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PySR
High-Performance Symbolic Regression in Python and Julia
...The details of these algorithms are described in the PySR paper. Symbolic regression works best on low-dimensional datasets, but one can also extend these approaches to higher-dimensional spaces by using "Symbolic Distillation" of Neural Networks, as explained in 2006.11287, where we apply it to N-body problems. Here, one essentially uses symbolic regression to convert a neural net to an analytic equation. Thus, these tools simultaneously present an explicit and powerful way to interpret deep neural networks. -
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Loki
Visual Regression Testing for Storybook
There are a few visual regression tools for the web, but most either cannot be run headless or use phantomjs which is deprecated and a browser nobody is actually using. They usually also require you to maintain fixtures. With react-native it's now possible to target multiple platforms with a single code base, but there's no single tool to test all to my knowledge. -
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caret
caret (Classification And Regression Training) R package
The caret (Classification And Regression Training) R package streamlines the process of building predictive machine learning models. It provides uniform interfaces for model training, tuning, evaluation, preprocessing, and variable importance. With support for over 200 models, caret is foundational for R workflows in modeling and machine learning. -
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Linfa
A Rust machine learning framework
linfa aims to provide a comprehensive toolkit to build Machine Learning applications with Rust. Kin in spirit to Python's scikit-learn, it focuses on common preprocessing tasks and classical ML algorithms for your everyday ML tasks. -
12
Happo
Visual diffing in CI for user interfaces
Happo is a visual regression testing tool. It hooks into your CI environment to compare the visual appearance of UI components before and after a change. Screenshots are taken in different browsers and across different screen sizes to ensure consistent cross-browser and responsive styling of your application. The first thing you want to do is to set up a test suite for Happo. -
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Selenium
Browser automation framework and ecosystem
...Primarily it is for automating web applications for testing purposes, but is certainly not limited to just that. Boring web-based administration tasks can (and should) also be automated as well. If you want to create robust, browser-based regression automation suites and tests, scale and distribute scripts across many environments, then you want to use Selenium WebDriver, a collection of language specific bindings to drive a browser - the way it is meant to be driven. If you want to create quick bug reproduction scripts, create scripts to aid in automation-aided exploratory testing, then you want to use Selenium IDE; a Chrome and Firefox add-on that will do simple record-and-playback of interactions with the browser. ... -
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gplearn
Genetic Programming in Python, with a scikit-learn inspired API
gplearn implements Genetic Programming in Python, with a scikit-learn-inspired and compatible API. While Genetic Programming (GP) can be used to perform a very wide variety of tasks, gplearn is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straightforward to implement. Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship. It begins by building a population of naive random formulas to represent a relationship between known independent variables and their dependent variable targets in order to predict new data. ... -
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LIBSVM.jl
LIBSVM bindings for Julia
LIBSVM bindings for Julia. This is a Julia interface for LIBSVM and for the linear SVM model provided by LIBLINEAR. Supports all LIBSVM models: classification C-SVC, nu-SVC, regression: epsilon-SVR, nu-SVR and distribution estimation: one-class SVM. Model objects are represented by Julia-type SVM which gives you easy access to model features and can be saved e.g. as JLD file. -
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Bayesian Statistics
This repository holds slides and code for a full Bayesian statistics
This repository holds slides and code for a full Bayesian statistics graduate course. Bayesian statistics is an approach to inferential statistics based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used... -
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Storycap
A Storybook Addon, Save the screenshot image of your stories
Storycap crawls your Storybook and takes screenshot images. It is primarily responsible for image generation necessary for Visual Testing such as reg-suit. -
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Machine learning basics
Plain python implementations of basic machine learning algorithms
...Instead of relying on external machine learning libraries, the algorithms are implemented from scratch so that users can explore the mathematical logic and computational structure behind each technique. The repository includes notebooks that demonstrate classic algorithms such as linear regression, logistic regression, k-nearest neighbors, decision trees, support vector machines, and clustering techniques. Each notebook typically combines explanatory text, Python code, and visualizations to illustrate how the algorithm operates and how it can be applied to datasets. -
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Zero to Mastery Machine Learning
All course materials for the Zero to Mastery Machine Learning
...The course introduces essential tools such as NumPy, pandas, Matplotlib, and scikit-learn before moving on to deep learning with frameworks like TensorFlow and Keras. It also includes milestone projects that demonstrate how to build end-to-end machine learning systems using real datasets, including classification and regression tasks. -
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Playbook
A library for isolated developing UI components
...This allows you to not only review UI quickly but also deliver more robust designs by separating business logics out of components. Besides, snapshots of each component can be automatically generated by unit tests, and visual regression testing can be performed using arbitrary third-party tools. For complex modern app development, it’s important to catch UI changes more sensitively and keep improving them faster. With the Playbook, you don't have to struggle through preparing the data and spend human resources for manual testings. -
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Coursera-ML-AndrewNg-Notes
Personal notes from Wu Enda's machine learning course
...The project aims to help students understand the mathematical concepts, algorithms, and intuition behind fundamental machine learning techniques taught in the course. It organizes the material into clear written summaries that accompany each lecture topic, including supervised learning, regression methods, neural networks, and optimization algorithms. The repository often expands on the original lecture material by adding additional explanations, diagrams, and formulas that clarify the theoretical foundations of the algorithms. These notes serve as a structured reference that learners can review while studying or revisiting machine learning fundamentals. -
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XGBoost
Scalable and Flexible Gradient Boosting
XGBoost is an optimized distributed gradient boosting library, designed to be scalable, flexible, portable and highly efficient. It supports regression, classification, ranking and user defined objectives, and runs on all major operating systems and cloud platforms. XGBoost works by implementing machine learning algorithms under the Gradient Boosting framework. It also offers parallel tree boosting (GBDT, GBRT or GBM) that can quickly and accurately solve many data science problems. ... -
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scikit-learn
Machine learning in Python
scikit-learn is an open source Python module for machine learning built on NumPy, SciPy and matplotlib. It offers simple and efficient tools for predictive data analysis and is reusable in various contexts. -
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TabPFN
Foundation Model for Tabular Data
...The model is based on transformer architectures and implements a prior-data fitted network that can perform supervised learning tasks such as classification and regression with minimal configuration. Unlike many traditional machine learning workflows that require extensive hyperparameter tuning and training cycles, TabPFN is pre-trained to perform inference directly on tabular datasets. This allows it to generate predictions extremely quickly, often within seconds, while maintaining competitive accuracy on small and medium-sized datasets. ... -
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Hermione
Browser test runner based on mocha and wdio
Hermione is a utility for integration testing of web pages using WebdriverIO and Mocha. If you are familiar with WebdriverIO and Mocha, you can start writing and running tests in 5 minutes! You need to install Hermione via npm and add a tiny config to your project. When tests are run one by one, it takes a lot of time. Hermione can run tests in parallel sessions in different browsers out of the box. Running of too many tests in parallel can lead to the overloading of the main process CPU...
