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Title: GitHub - PedroSeber/SmartProcessAnalytics: Smart Process Analytics (SPA) is a software package for automatic machine learning. Given user-input data (and optional user preferences), SPA automatically cross-validates and tests ML and DL models. Model types are selected based on the properties of the data, minimizing the risk of data-specific variance.

Open Graph Title: GitHub - PedroSeber/SmartProcessAnalytics: Smart Process Analytics (SPA) is a software package for automatic machine learning. Given user-input data (and optional user preferences), SPA automatically cross-validates and tests ML and DL models. Model types are selected based on the properties of the data, minimizing the risk of data-specific variance.

X Title: GitHub - PedroSeber/SmartProcessAnalytics: Smart Process Analytics (SPA) is a software package for automatic machine learning. Given user-input data (and optional user preferences), SPA automatically cross-validates and tests ML and DL models. Model types are selected based on the properties of the data, minimizing the risk of data-specific variance.

Description: Smart Process Analytics (SPA) is a software package for automatic machine learning. Given user-input data (and optional user preferences), SPA automatically cross-validates and tests ML and DL models. Model types are selected based on the properties of the data, minimizing the risk of data-specific variance. - PedroSeber/SmartProcessAnalytics

Open Graph Description: Smart Process Analytics (SPA) is a software package for automatic machine learning. Given user-input data (and optional user preferences), SPA automatically cross-validates and tests ML and DL mode...

X Description: Smart Process Analytics (SPA) is a software package for automatic machine learning. Given user-input data (and optional user preferences), SPA automatically cross-validates and tests ML and DL mode...

Opengraph URL: https://github.com/PedroSeber/SmartProcessAnalytics

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LCEN: A Novel Feature Selection Algorithm for Nonlinear, Interpretable Machine Learning Modelshttps://arxiv.org/abs/2402.17120
Improving N-Glycosylation and Biopharmaceutical Production Predictions Using AutoML-Built Residual Hybrid Modelshttps://doi.org/10.1101/2024.08.27.609988
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