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Ensemble Machine Learning with R

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Ensemble Machine Learning with R
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15
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CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Production Year2023
Production PlaceWageningen

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Abstract
Tom Hengl is the co-founder of OpenGeoHub Foundation, the Netherlands, and leader of the Work Package “Dissemination, project sustainability, and impact assessment” of the MOOD project. As a PhD candidate and research within OpenGeoHub Foundation, Carmelo focuses on data science projects such as GeoHarmonizer and the MOOD H2020 project. During the 2023 MOOD Summer School, these two experts gave a lecture where the students learned how to combine all the tools that were introduced in the previous lectures: point data, predictor variables, different modeling and validation techniques. The algorithms explored in the previous lectures are here combined to produce results that are in general more robust than when using a single algorithm only.
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