Search Results for "regression"
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Showing 351 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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Selenium
Browser automation framework and ecosystem
Selenium automates browsers. That's it! What you do with that power is entirely up to you. 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... -
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dlib
Toolkit for making machine learning and data analysis applications
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib's open source licensing allows you to use it in any application, free of charge. Good unit test coverage, the ratio of unit test lines of code to library lines of code is... -
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NGBoost
Natural Gradient Boosting for Probabilistic Prediction
ngboost is a Python library that implements Natural Gradient Boosting, as described in "NGBoost: Natural Gradient Boosting for Probabilistic Prediction". It is built on top of Scikit-Learn and is designed to be scalable and modular with respect to the choice of proper scoring rule, distribution, and base learner. A didactic introduction to the methodology underlying NGBoost is available in this slide deck. -
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nuclei
Fast and customizable vulnerability scanner based on simple YAML
Nuclei is used to send requests across targets based on a template, leading to zero false positives and providing fast scanning on a large number of hosts. Nuclei offers scanning for a variety of protocols, including TCP, DNS, HTTP, SSL, File, Whois, Websocket, Headless etc. With powerful and flexible templating, Nuclei can be used to model all kinds of security checks. We have a dedicated repository that houses various type of vulnerability templates contributed by more than 300 security... -
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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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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. XGBoost... -
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Smile
Statistical machine intelligence and learning engine
Smile is a fast and comprehensive machine learning engine. With advanced data structures and algorithms, Smile delivers the state-of-art performance. Compared to this third-party benchmark, Smile outperforms R, Python, Spark, H2O, xgboost significantly. Smile is a couple of times faster than the closest competitor. The memory usage is also very efficient. If we can train advanced machine learning models on a PC, why buy a cluster? Write applications quickly in Java, Scala, or any JVM... -
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LightGBM
Gradient boosting framework based on decision tree algorithms
LightGBM or Light Gradient Boosting Machine is a high-performance, open source gradient boosting framework based on decision tree algorithms. Compared to other boosting frameworks, LightGBM offers several advantages in terms of speed, efficiency and accuracy. Parallel experiments have shown that LightGBM can attain linear speed-up through multiple machines for training in specific settings, all while consuming less memory. LightGBM supports parallel and GPU learning, and can handle... -
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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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SymbolicRegression.jl
Distributed High-Performance Symbolic Regression in Julia
SymbolicRegression.jl searches for symbolic expressions which optimize a particular objective. -
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nut.js
Native UI testing / controlling with node
nut.js gives you full control over your mouse. Move, click or drag your cursor where you need it! Press (and hold) single keys or type pages of text, nut.js handles both! It allows for native UI interactions via keyboard and/or mouse but additionally gives you the possibility to navigate the screen based on image matching. nut.js gives you access to your system clipboard. Copy and paste text as you go! Retrieve info about open windows to improve your tests or workflows. nut.js provides... -
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Playbook
A library for isolated developing UI components
... 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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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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statsmodels
Statsmodels, statistical modeling and econometrics in Python
statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. Generalized linear models with support for all... -
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CatBoost
High-performance library for gradient boosting on decision trees
CatBoost is a fast, high-performance open source library for gradient boosting on decision trees. It is a machine learning method with plenty of applications, including ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. CatBoost offers superior performance over other GBDT libraries on many datasets, and has several superb features. It has best in class prediction speed, supports both numerical and categorical features, has a fast and scalable GPU... -
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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. While Detox focuses on end-to-end testing in general, React Native Owl focuses on visual regression... -
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FLAML
A fast library for AutoML and tuning
FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically. It frees users from selecting learners and hyperparameters for each learner. For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. It supports both classical machine learning models and deep neural networks. It is easy to customize or extend. Users can find... -
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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. -
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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... -
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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. Loki aims to have easy setup, no to low maintenance cost, reproducible tests independent of which OS they are run on, runnable on CI... -
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PySR
High-Performance Symbolic Regression in Python and Julia
PySR is an open-source tool for Symbolic Regression: a machine learning task where the goal is to find an interpretable symbolic expression that optimizes some objective. Over a period of several years, PySR has been engineered from the ground up to be (1) as high-performance as possible, (2) as configurable as possible, and (3) easy to use. PySR is developed alongside the Julia library SymbolicRegression.jl, which forms the powerful search engine of PySR. The details of these algorithms... -
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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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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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tsai
Time series Timeseries Deep Learning Machine Learning Pytorch fastai
tsai is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series tasks like classification, regression, forecasting, and imputation. Starting with tsai 0.3.0 tsai will only install hard dependencies. Other soft dependencies (which are only required for selected tasks) will not be installed by default (this is the recommended approach. If you require any of the dependencies that is not installed, tsai will ask you to install...
