Search Results for "test"
Sort By:
Showing 3 open source projects for "test"
View related business solutions-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
Add a fully functional PDF editor to your ASP.NET website in minutes!RAD PDF natively supports the most commonly used PDF features. Beyond a PDF reader, RAD PDF equips web applications with a browser-based PDF editor & PDF annotator. Features not even available in Adobe Acrobat Reader can be used to provide advanced PDF functionality. By enabling PDF form filling & PDF form saving directly in the web browser, RAD PDF users can utilize PDF features without worrying about having the right platform or software installed! Allow your app to design new PDF forms, protect content, sign PDFs, & more using an intuitive graphical user interface (GUI).
-
1
performance
Models' quality and performance metrics (R2, ICC, LOO, AIC, BF, ...)
performance is part of the easystats ecosystem and offers model quality assessment tools for R. It computes metrics like R², RMSE, ICC, and conducts diagnostics such as overdispersion, zero‑inflation, convergence, and singularity checks, complementing model workflows with comprehensive evaluation. -
2
R Packages (r-pkgs)
Building R packages
rpkgs (in GitHub via hadley/r-pkgs) is the source (text + examples) for the book R Packages by Hadley Wickham and Jenny Bryan. The book teaches how to develop, document, test, and share R packages: the practices, tools, infrastructure, workflows, and best practices around package development in R. The repository contains the code, text, site content for building the book, examples, exercises, etc. It is not a software library to be loaded in R (except perhaps the examples), but a resource/guide/manual. ... -
3
benchm-ml
A benchmark of commonly used open source implementations
...It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. The benchmarks cover algorithms like logistic regression, random forest, gradient boosting, and deep neural networks, and they compare across toolkits such as scikit-learn, R packages, xgboost, H2O, Spark MLlib, etc. The repository is structured in logical folders, each corresponding to algorithm categories.
- Previous
- You're on page 1
- Next
