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@prefix swo:   <http://www.ebi.ac.uk/efo/swo/> .
@prefix cito:  <http://purl.org/spar/cito/> .
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@prefix ocresst: <http://purl.org/net/OCRe/statistics.owl#> .
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@prefix vann:  <http://purl.org/vocab/vann/> .
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@prefix fabio: <http://purl.org/spar/fabio/> .
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@prefix rdf:   <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix ocresp: <http://purl.org/net/OCRe/study_protocol.owl#> .
@prefix bibo:  <http://purl.org/ontology/bibo/> .
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        thkoeln:subtitle        "Tuning of Gradient Boosting (xgboost)" ;
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        bibo:abstract           "This case study gives a hands-on description of Hyperparameter Tuning (HPT) methods discussed in this book. The Extreme Gradient Boosting (XGBoost) method and its implementation xgboost was chosen, because it is one of the most powerful methods in many Machine Learning (ML) tasks, especially when standard tabular data should be analyzed. This case study follows the same HPT pipeline as the first and third studies: after the data set is provided and pre-processed, the experimental design is set up. Next, the HPT experiments are performed. The R package SPOT is used as a “datascope” to analyze the results from the HPT runs from several perspectives: in addition to Classification and Regression Trees (CART), the analysis combines results from the surface, sensitivity, and parallel plots with a classical regression analysis. Severity is used to discuss the practical relevance of the results from an error-statistical point-of-view. The well-proven R package mlr is used as a uniform interface from the methods of the packages SPOT and SPOTMisc to the ML methods. The corresponding source code is explained in a comprehensible manner." ;
        bibo:doi                "10.1007/978-981-19-5170-1_9" ;
        bibo:isbn13             "9789811951725" ;
        bibo:pageEnd            "234" ;
        bibo:pageStart          "221" ;
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