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PMML - Machine Learning Case - MLP - Apply via Python

<p>A data preparation and model building for "RProp MLP Learner" - also apply the resulting PMML model file with Python</p>

URL: Parameter Optimization Video - KNIME TV - YouTube https://youtu.be/IlqepyIba6Y
URL: ML Algorithms and the Art of Parameter Selection - KNIME Blog https://www.knime.com/blog/machine-learning-algorithms-and-the-art-of-hyperparameter-selection
URL: Medium: About Machine Learning — How it Fails and Succeeds https://medium.com/low-code-for-advanced-data-science/about-machine-learning-how-it-fails-and-succeeds-9f3ab7cb9b00
URL: Medium: KNIME — Machine Learning and Artificial Intelligence — A Collection https://medium.com/low-code-for-advanced-data-science/knime-machine-learning-and-artificial-intelligence-a-collection-12e0f7d83b50
URL: KNIME Forum (83474) - apply PMML Model via Python https://forum.knime.com/t/pmml-export-usage-at-python-enviroment/83474/2?u=mlauber71
URL: PMML models providing probabilities - KNIME Forum (13244) https://forum.knime.com/t/export-pmml-that-outputs-class-probabilities/13244/2?u=mlauber71

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: kathrinmelcher
Created at: 2017-09-14
On NodePit since: 2024-09-26
Last update: 2024-10-04
Created with KNIME version: v5.3.2
Tags: machine learningdata sciencedeep learningneural networkRPropMLPNeural NetworkPMMLpypmml

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