Displaying 1 to 9 from 9 results

mlr - mlr: Machine Learning in R

  •    R

Please cite our JMLR paper [bibtex]. Some parts of the package were created as part of other publications. If you use these parts, please cite the relevant work appropriately. An overview of all mlr related publications can be found here.

mlens - ML-Ensemble – high performance ensemble learning

  •    Python

ML-Ensemble combines a Scikit-learn high-level API with a low-level computational graph framework to build memory efficient, maximally parallelized ensemble networks in as few lines of codes as possible. ML-Ensemble is thread safe as long as base learners are and can fall back on memory mapped multiprocessing for memory-neutral process-based concurrency. For tutorials and full documentation, visit the project website.




StickyStack

  •    Javascript

A jQuery plugin that creates a stacking effect by sticking panels as they reach the top of the viewport. First include jQuery, then call .stickyStack() on the main content wrapper (or define it using options). Note that the stackingElements should be direct children of the containerElement.

AutoMLPipeline

  •    Julia

AutoMLPipeline is a package that makes it trivial to create complex ML pipeline structures using simple expressions. It leverages on the built-in macro programming features of Julia to symbolically process, manipulate pipeline expressions, and makes it easy to discover optimal structures for machine learning regression and classification. Just take note that + has higher priority than |> so if you are not sure, enclose the operations inside parentheses.

xam - :dart: Personal data science and machine learning toolbox

  •    Python

xam is my personal data science and machine learning toolbox. It is written in Python 3 and stands on the shoulders of giants (mainly pandas and scikit-learn). It loosely follows scikit-learn's fit/transform/predict convention. ⚠️ Because xam is a personal toolkit, the --upgrade flag will install the latest releases of each dependency (scipy, pandas etc.). I like to stay up-to-date with the latest library versions.


explorer - Explore transactions and accounts on the Stacks blockchain

  •    TypeScript

The Stacks Explorer is built with react, next.js and @stacks/ui. To run the explorer locally, you can clone this repo and install the dependencies needed. Make sure you have yarn installed. To build and run the application, you can run this yarn task which will launch the application at http://localhost:3000.






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