Abstract
In this chapter, we continue with supervised learning and tree-based regression. Specifically, we develop a gradient-boosted tree (GBT) regression model using the same housing dataset we used for decision tree and random forest regression in the preceding chapters. This way, we can have a better idea about which tree type performs better by comparing their performance metrics.
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Testas, A. (2023). Gradient-Boosted Tree Regression with Pandas, Scikit-Learn, and PySpark. In: Distributed Machine Learning with PySpark. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-9751-3_6
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DOI: https://doi.org/10.1007/978-1-4842-9751-3_6
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Publisher Name: Apress, Berkeley, CA
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Online ISBN: 978-1-4842-9751-3
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