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Applied Machine Learning Using mlr3 in R
mlr3 is an award-winning ecosystem of R packages that have been developed to enable state-of-the-art machine learning capabilities in R. Applied Machine Learning Using mlr3 in R gives an overview of flexible and robust machine learning methods, with an emphasis on how to implement them using mlr3 in R. It covers various key topics, including basic machine learning tasks, such as building and evaluating a predictive model; hyperparameter tuning of machine learning approaches to obtain peak performance; building machine learning pipelines that perform complex operations such as pre-processing followed by modelling followed by aggregation of predictions; and extending the mlr3 ecosystem with cu...
Applied Machine Learning Using mlr3 in R
mlr3 is an award-winning ecosystem of R packages that have been developed to enable state-of-the-art machine learning capabilities in R. Applied Machine Learning Using mlr3 in R gives an overview of flexible and robust machine learning methods, with an emphasis on how to implement them using mlr3 in R. It covers various key topics, including basic machine learning tasks, such as building and evaluating a predictive model; hyperparameter tuning of machine learning approaches to obtain peak performance; building machine learning pipelines that perform complex operations such as pre-processing followed by modelling followed by aggregation of predictions; and extending the mlr3 ecosystem with cu...
Big Data Analytics in Oncology with R
Big Data Analytics in Oncology with R serves the analytical approaches for big data analysis. There is huge progressed in advanced computation with R. But there are several technical challenges faced to work with big data. These challenges are with computational aspect and work with fastest way to get computational results. Clinical decision through genomic information and survival outcomes are now unavoidable in cutting-edge oncology research. This book is intended to provide a comprehensive text to work with some recent development in the area. Features: Covers gene expression data analysis using R and survival analysis using R Includes bayesian in survival-gene expression analysis Discusses competing-gene expression analysis using R Covers Bayesian on survival with omics data This book is aimed primarily at graduates and researchers studying survival analysis or statistical methods in genetics.
DAGStat 2022
Das Buch enthält die Abstracts der eingeladenen bzw. angenommenen Vorträge der 6. Konferenz der Deutschen Arbeitsgemeinschaft Statistik (DAGStat), welche vom 28. März bis 1. April 2022 am Universitätsklinikum Hamburg-Eppendorf (UKE) in Kooperation mit der Universität Hamburg sowie der Helmut-Schmidt-Universität stattfand. Die Konferenz stellte ebenfalls das 68. Biometrische Kolloquium der Deutschen Region der International Biometric Society (IBS-DR) dar, sowie die 45. Jahrestagung der Gesellschaft für Klassifikation (GfKl/Data Science Society). Die Vorträge behandelten dabei ein breites Spektrum sowohl angewandter als auch eher methodischer/theoretischer Themen aus dem Bereich Statistik und Data Science.
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