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Published by Academic Press 2023-02-08, 2023
ISBN 10: 012824271X ISBN 13: 9780128242711
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Published by Elsevier Science Publishing Co Inc, 2023
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Published by Elsevier Science Publishing Co Inc, US, 2023
ISBN 10: 012824271X ISBN 13: 9780128242711
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Paperback. Condition: New. Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning. In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear.
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Published by Elsevier Science Publishing Co Inc, 2023
ISBN 10: 012824271X ISBN 13: 9780128242711
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Published by Elsevier Science Publishing Co Inc Jan 2023, 2023
ISBN 10: 012824271X ISBN 13: 9780128242711
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Taschenbuch. Condition: Neu. Neuware - Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning. In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear.
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Published by Elsevier Science Publishing Co Inc, 2023
ISBN 10: 012824271X ISBN 13: 9780128242711
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Taschenbuch. Condition: Neu. Data Science, Analytics and Machine Learning with R | Luiz Paulo Favero (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2023 | Elsevier Science Publishing Co Inc | EAN 9780128242711 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Language: English
Published by Elsevier Science Publishing Co Inc, US, 2023
ISBN 10: 012824271X ISBN 13: 9780128242711
Seller: Rarewaves.com UK, London, United Kingdom
Paperback. Condition: New. Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning. In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear.
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