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changed the long description to trigger the deployment actions #45

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Dec 16, 2024
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changed the long description to trigger the deployment actions #45

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26 changes: 14 additions & 12 deletions tools/.shed.yml
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Expand Up @@ -4,12 +4,13 @@ description: A powerful and robust low-code library for simplifying machine lear
name: galaxy_pycaret
owner: goeckslab
long_description: |
PyCaret is a low-code machine learning library that simplifies the process of building, training, and deploying machine learning models.
With its comprehensive suite of tools, PyCaret enables users to perform complex tasks such as data preprocessing,
model selection, hyperparameter tuning, and evaluation, all within a single, intuitive interface.
It is designed to be powerful and robust, making it suitable for both beginners and advanced users.
Whether you are exploring data for insights, building predictive models, or deploying them into production,
PyCaret provides a streamlined workflow that saves time and reduces code complexity.
Galaxy-PyCaret brings the power of PyCaret to the Galaxy platform,
focusing on essential machine learning tasks like model comparison,
prediction, and evaluation. It enables users to easily analyze data,
compare algorithms, and generate predictions with minimal coding.
Designed for simplicity and efficiency,
Galaxy-PyCaret is ideal for researchers and
data scientists seeking streamlined workflows for machine learning tasks within Galaxy.
remote_repository_url: https://github.com/goeckslab/Galaxy-Pycaret
homepage_url: https://github.com/goeckslab/Galaxy-Pycaret

Expand All @@ -20,9 +21,10 @@ suite:
name: suite_pycaret
description: A powerful and robust low-code library for simplifying machine learning workflows with PyCaret.
long_description: |
PyCaret is a low-code machine learning library that simplifies the process of building, training, and deploying machine learning models.
With its comprehensive suite of tools, PyCaret enables users to perform complex tasks such as data preprocessing,
model selection, hyperparameter tuning, and evaluation, all within a single, intuitive interface.
It is designed to be powerful and robust, making it suitable for both beginners and advanced users.
Whether you are exploring data for insights, building predictive models, or deploying them into production,
PyCaret provides a streamlined workflow that saves time and reduces code complexity.
Galaxy-PyCaret brings the power of PyCaret to the Galaxy platform,
focusing on essential machine learning tasks like model comparison,
prediction, and evaluation. It enables users to easily analyze data,
compare algorithms, and generate predictions with minimal coding.
Designed for simplicity and efficiency,
Galaxy-PyCaret is ideal for researchers and
data scientists seeking streamlined workflows for machine learning tasks within Galaxy.
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