Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

# palladio ParALleL frAmework for moDel selectIOn

### Welcome to PALLADIO. PALLADIO is a machine learning framework whose purpose is to provide robust and reproducible results when dealing with data where the signal to noise ratio is low; it also provides tools to determine whether the dataset being analyzed contains any signal at all. PALLADIO works by repeating the same experiment many times, each time resampling the training and the test set so that the outcome is reliable as it is not determined by a single partition of the dataset. Besides, using permutation tests, a measure of how much experiments produce a reliable result is provided. Since all experiments performed are independent, PALLADIO is designed so that it can exploit a cluster where it is available.

### Dependencies PALLADIO is developed using Python 2.7 and inherits its main functionalities from: * numpy * scipy * scikit-learn * mpi4py * matplotlib * seaborn

### Authors and Contributors Current developers: Matteo Barbieri (@matteobarbieri), Samuele Fiorini (@samuelefiorini) and Federico Tomasi (@fdtomasi).

### Support or Contact Having trouble with PALLADIO? Check out our [documentation](http://slipguru.github.io/palladio/) or contact us: * matteo [dot] barbieri [at] dibris [dot] unige [dot] it * samuele [dot] fiorini [at] dibris [dot] unige [dot] it * federico [dot] tomasi [at] dibris [dot] unige [dot] it

Metadata

Release files for palladio 2.0.4rc1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for palladio 2.0.4rc1
File Interpreter ABI Platform
palladio-2.0.4rc1-py2.py3-none-any.whl Python 2, Python 3 none any Details

Release files / palladio-2.0.4rc1-py2.py3-none-any.whl

Download URL palladio-2.0.4rc1-py2.py3-none-any.whl
Size 53.8 kB
Tags Python 2 Python 3
SHA-256 checksum
How to use checksums
88037d4c8af457b5b69276c7525d78ebfd4cd2beaf46637dd15aac79138d1060
BLAKE2b-256 checksum
How to use checksums
6879bae68c246fff9b18c787558b87a6f63878771f381187efd5a33c1dc13116
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page