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Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector (SpringerBriefs in Applied Sciences and Technology) - Tapa blanda
Sinopsis
This book presents machine learning approaches to identify the most important predictors of crucial variables for dealing with the challenges of managing production units and designing agriculture policies. The book focuses on the agricultural sector in the European Union and considers statistical information from the Farm Accountancy Data Network (FADN).
Presently, statistical databases present a lot of information for many indicators and, in these contexts, one of the main tasks is to identify the most important predictors of certain indicators. In this way, the book presents approaches to identifying the most relevant variables that best support the design of adjusted farming policies and management plans. These subjects are currently important for students, public institutions and farmers. To achieve these objectives, the book considers the IBM SPSS Modeler procedures as well as the respective models suggested by this software.
The book is read by students in production engineering, economics and agricultural studies, public bodies and managers in the farming sector.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Vítor João Pereira Domingues Martinho is Coordinator Professor with Habilitation at the Polytechnic Institute of Viseu, Portugal. He received his Ph.D. in Economics from the University of Coimbra, Portugal. With a profound academic background, he has also served as Invited Professor at the University of Trás-os-Montes and Alto Douro (Portugal) and currently holds the position of Invited Professor at the Polytechnic Institute of Coimbra (Portugal). From 2006 to 2012, Professor Martinho served as President of the Scientific Council, President of the Directive Council, and President of the Agricultural School belonging to the Polytechnic Institute of Viseu, Portugal. Additionally, he was President of the Direction of the Association of Forest Producers of Viseu, Portugal. Martinho has published several technical and scientific papers and serves as Referee for various scientific and technical journals. He actively participates in the evaluation of national and international projects.
De la contraportada
This book presents machine learning approaches to identify the most important predictors of crucial variables for dealing with the challenges of managing production units and designing agriculture policies. The book focuses on the agricultural sector in the European Union and considers statistical information from the Farm Accountancy Data Network (FADN).
Presently, statistical databases present a lot of information for many indicators and, in these contexts, one of the main tasks is to identify the most important predictors of certain indicators. In this way, the book presents approaches to identifying the most relevant variables that best support the design of adjusted farming policies and management plans. These subjects are currently important for students, public institutions and farmers. To achieve these objectives, the book considers the IBM SPSS Modeler procedures as well as the respective models suggested by this software.
The book is read by students in production engineering, economics and agricultural studies, public bodies and managers in the farming sector.
"Sobre este título" puede pertenecer a otra edición de este libro.
- EditorialSpringer
- Año de publicación2024
- ISBN 10 3031546075
- ISBN 13 9783031546075
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de páginas148
- Contacto del fabricanteno disponible
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Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector (SpringerBriefs in Applied Sciences and Technology)
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Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector
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Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector
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Springer Nature Switzerland, Springer International Publishing Feb 2024, 2024
ISBN 10: 3031546075
ISBN 13: 9783031546075
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents machine learning approaches to identify the most important predictors of crucial variables for dealing with the challenges of managing production units and designing agriculture policies. The book focuses on the agricultural sector in the European Union and considers statistical information from the Farm Accountancy Data Network (FADN).Presently, statistical databases present a lot of information for many indicators and, in these contexts, one of the main tasks is to identify the most important predictors of certain indicators. In this way, the book presents approaches to identifying the most relevant variables that best support the design of adjusted farming policies and management plans. These subjects are currently important for students, public institutions and farmers. To achieve these objectives, the book considers the IBM SPSS Modeler procedures as well as the respective models suggested by this software.The book is read by students in production engineering, economics and agricultural studies, public bodies and managers in the farming sector. 148 pp. Englisch. Nº de ref. del artículo: 9783031546075
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Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector
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Springer Nature Switzerland, Springer Nature Switzerland, 2024
ISBN 10: 3031546075
ISBN 13: 9783031546075
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Taschenbuch
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents machine learning approaches to identify the most important predictors of crucial variables for dealing with the challenges of managing production units and designing agriculture policies. The book focuses on the agricultural sector in the European Union and considers statistical information from the Farm Accountancy Data Network (FADN).Presently, statistical databases present a lot of information for many indicators and, in these contexts, one of the main tasks is to identify the most important predictors of certain indicators. In this way, the book presents approaches to identifying the most relevant variables that best support the design of adjusted farming policies and management plans. These subjects are currently important for students, public institutions and farmers. To achieve these objectives, the book considers the IBM SPSS Modeler procedures as well as the respective models suggested by this software.The book is read by students in production engineering, economics and agricultural studies, public bodies and managers in the farming sector. Nº de ref. del artículo: 9783031546075
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Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector
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Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector
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ISBN 10: 3031546075
ISBN 13: 9783031546075
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book presents machine learning approaches to identify the most important predictors of crucial variables for dealing with the challenges of managing production units and designing agriculture policies. The book focuses on the agricultural sector in . Nº de ref. del artículo: 1362013405
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Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector (SpringerBriefs in Applied Sciences and Technology)
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Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector
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Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector (Paperback)
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Springer International Publishing AG, Cham, 2024
ISBN 10: 3031546075
ISBN 13: 9783031546075
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Paperback. Condición: new. Paperback. This book presents machine learning approaches to identify the most important predictors of crucial variables for dealing with the challenges of managing production units and designing agriculture policies. The book focuses on the agricultural sector in the European Union and considers statistical information from the Farm Accountancy Data Network (FADN).Presently, statistical databases present a lot of information for many indicators and, in these contexts, one of the main tasks is to identify the most important predictors of certain indicators. In this way, the book presents approaches to identifying the most relevant variables that best support the design of adjusted farming policies and management plans. These subjects are currently important for students, public institutions and farmers. To achieve these objectives, the book considers the IBM SPSS Modeler procedures as well as the respective models suggested by this software.The book is read by students in production engineering, economics and agricultural studies, public bodies and managers in the farming sector. This book presents machine learning approaches to identify the most important predictors of crucial variables for dealing with the challenges of managing production units and designing agriculture policies. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9783031546075
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