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Project description
PHOTONAI is a rapid prototyping framework enabling (not so experienced) users to build, train, optimize, evaluate, and share even complex machine learning (ML) pipelines with very high efficiency.
By pre-registering state-of-the-art ML implementations, we create a system in which the user can select and arrange processing steps and learning algorithms in simple or parallel pipeline data streams.
Importantly, PHOTONAI is capable to automatize the training and testing procedure including nested cross-validation and hyperparameter search, calculates performance metrics and conveniently visualizes the analyzed hyperparameter space.
It also enables the user persist and load your optimal model, including all preprocessing elements, with only one line of code.
Home-page: https://github.com/mmll-wwu/photonai.git Author: PHOTONAI Team Author-email: hahnt@wwu.de License: UNKNOWN Download-URL: https://github.com/wwu-mmll/photonai/archive/2.0.0.tar.gz Description: UNKNOWN Keywords: machine learning,deep learning,neural networks,hyperparameter Platform: UNKNOWN
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