Decision tree analysis - Revision history

S222540: /* Annotated bibliography */

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Annotated bibliography

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	=='''Annotated bibliography'''==		=='''Annotated bibliography'''==
	'''Kapil Mittal, Dinesh Khanduja and Puran Chandra Tewari. An Insight into “Decision Tree Analysis”. 2017.'''		'''Kapil Mittal, Dinesh Khanduja and Puran Chandra Tewari. An Insight into “Decision Tree Analysis”. 2017.'''<br/>
	This article discusses the concept of decision tree analysis as a technique to help with decision-making, both in personal and professional contexts. The authors provide an overview of the history and evolution of decision tree analysis and its application in various industries. They also describe the advantages and disadvantages of using this methodology and provide examples to demonstrate the positive effects decision tree analysis has on productivity in an industrial environment.		This article discusses the concept of decision tree analysis as a technique to help with decision-making, both in personal and professional contexts. The authors provide an overview of the history and evolution of decision tree analysis and its application in various industries. They also describe the advantages and disadvantages of using this methodology and provide examples to demonstrate the positive effects decision tree analysis has on productivity in an industrial environment.

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	This article provides an overview of the decision tree methodology. The authors discuss the algorithm's non-parametric nature and its ability to handle large, complex datasets. They also describe how the methodology can be used to divide datasets into training and validation sets, and introduce frequently used algorithms for developing decision trees. Finally, they describe the SPSS and SAS programs that can be used to visualize the tree structure.		This article provides an overview of the decision tree methodology. The authors discuss the algorithm's non-parametric nature and its ability to handle large, complex datasets. They also describe how the methodology can be used to divide datasets into training and validation sets, and introduce frequently used algorithms for developing decision trees. Finally, they describe the SPSS and SAS programs that can be used to visualize the tree structure.

	'''Prasanta Kumar Dey. Project risk management using multiple criteria decision-making technique and decision tree analysis: a case study of Indian oil refinery. 2012.'''		'''Prasanta Kumar Dey. Project risk management using multiple criteria decision-making technique and decision tree analysis: a case study of Indian oil refinery. 2012.'''<br/>
	This study proposes an integrated framework for managing project risks using a combination of various criteria decision-making techniques and decision tree analysis. The author develops a model for risk management, which he then applies through action research on a petroleum oil refinery construction project in India. The author uses cause and effect diagrams to identify risks, the analytic hierarchy process to analyze risks, and a risk map to develop responses. Decision tree analysis is then used to model different options for risk response development and to optimize the selection of a risk mitigating strategy. The proposed framework is designed to be easily adoptable and integrated with other project management knowledge areas.		This study proposes an integrated framework for managing project risks using a combination of various criteria decision-making techniques and decision tree analysis. The author develops a model for risk management, which he then applies through action research on a petroleum oil refinery construction project in India. The author uses cause and effect diagrams to identify risks, the analytic hierarchy process to analyze risks, and a risk map to develop responses. Decision tree analysis is then used to model different options for risk response development and to optimize the selection of a risk mitigating strategy. The proposed framework is designed to be easily adoptable and integrated with other project management knowledge areas.

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Annotated bibliography

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	This article discusses the concept of decision tree analysis as a technique to help with decision-making, both in personal and professional contexts. The authors provide an overview of the history and evolution of decision tree analysis and its application in various industries. They also describe the advantages and disadvantages of using this methodology and provide examples to demonstrate the positive effects decision tree analysis has on productivity in an industrial environment.		This article discusses the concept of decision tree analysis as a technique to help with decision-making, both in personal and professional contexts. The authors provide an overview of the history and evolution of decision tree analysis and its application in various industries. They also describe the advantages and disadvantages of using this methodology and provide examples to demonstrate the positive effects decision tree analysis has on productivity in an industrial environment.

	'''Yan-yan Song and Ying Lu. Decision tree methods: applications for classification and prediction. 2015.'''		'''Yan-yan Song and Ying Lu. Decision tree methods: applications for classification and prediction. 2015.''' <br/>
	This article provides an overview of the decision tree methodology. The authors discuss the algorithm's non-parametric nature and its ability to handle large, complex datasets. They also describe how the methodology can be used to divide datasets into training and validation sets, and introduce frequently used algorithms for developing decision trees. Finally, they describe the SPSS and SAS programs that can be used to visualize the tree structure.		This article provides an overview of the decision tree methodology. The authors discuss the algorithm's non-parametric nature and its ability to handle large, complex datasets. They also describe how the methodology can be used to divide datasets into training and validation sets, and introduce frequently used algorithms for developing decision trees. Finally, they describe the SPSS and SAS programs that can be used to visualize the tree structure.

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Annotated bibliography

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	'''Yan-yan Song and Ying Lu. Decision tree methods: applications for classification and prediction. 2015.'''		'''Yan-yan Song and Ying Lu. Decision tree methods: applications for classification and prediction. 2015.'''
	This article provides an overview of the decision tree methodology. The authors discuss the algorithm's non-parametric nature and its ability to handle large, complex datasets. They also describe how the methodology can be used to divide datasets into training and validation sets, and introduce frequently used algorithms for developing decision trees. Finally, they describe the SPSS and SAS programs that can be used to visualize the tree structure.		This article provides an overview of the decision tree methodology. The authors discuss the algorithm's non-parametric nature and its ability to handle large, complex datasets. They also describe how the methodology can be used to divide datasets into training and validation sets, and introduce frequently used algorithms for developing decision trees. Finally, they describe the SPSS and SAS programs that can be used to visualize the tree structure.

			'''Prasanta Kumar Dey. Project risk management using multiple criteria decision-making technique and decision tree analysis: a case study of Indian oil refinery. 2012.'''
			This study proposes an integrated framework for managing project risks using a combination of various criteria decision-making techniques and decision tree analysis. The author develops a model for risk management, which he then applies through action research on a petroleum oil refinery construction project in India. The author uses cause and effect diagrams to identify risks, the analytic hierarchy process to analyze risks, and a risk map to develop responses. Decision tree analysis is then used to model different options for risk response development and to optimize the selection of a risk mitigating strategy. The proposed framework is designed to be easily adoptable and integrated with other project management knowledge areas.

	=='''References'''==		=='''References'''==

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Annotated bibliography

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	=='''Annotated bibliography'''==		=='''Annotated bibliography'''==
	'''Kapil Mittal, Dinesh Khanduja and Puran Chandra Tewari. An Insight into “Decision Tree Analysis”. 2017.		'''Kapil Mittal, Dinesh Khanduja and Puran Chandra Tewari. An Insight into “Decision Tree Analysis”. 2017.'''
	This article discusses the concept of decision tree analysis as a technique to help with decision-making, both in personal and professional contexts. The authors provide an overview of the history and evolution of decision tree analysis and its application in various industries. They also describe the advantages and disadvantages of using this methodology and provide examples to demonstrate the positive effects decision tree analysis has on productivity in an industrial environment.		This article discusses the concept of decision tree analysis as a technique to help with decision-making, both in personal and professional contexts. The authors provide an overview of the history and evolution of decision tree analysis and its application in various industries. They also describe the advantages and disadvantages of using this methodology and provide examples to demonstrate the positive effects decision tree analysis has on productivity in an industrial environment.

			'''Yan-yan Song and Ying Lu. Decision tree methods: applications for classification and prediction. 2015.'''
			This article provides an overview of the decision tree methodology. The authors discuss the algorithm's non-parametric nature and its ability to handle large, complex datasets. They also describe how the methodology can be used to divide datasets into training and validation sets, and introduce frequently used algorithms for developing decision trees. Finally, they describe the SPSS and SAS programs that can be used to visualize the tree structure.

	=='''References'''==		=='''References'''==

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Limitations

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	=='''Limitations'''==		=='''Limitations'''==
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	<ref name="Mittal">Tewari, Puran & Mittal, Kapil & Khanduja, Dinesh. (2017). An Insight into “Decision Tree Analysis”. World Wide Journal of Multidisciplinary Research and Development. 3. 111-115. </ref>

	Decision trees have few disadvantages that can affect their usefulness in certain contexts. One major issue is their tendency to become overly complex, which can make them difficult for experts to interpret and can hinder decision-making processes, especially in high-stakes situations where incorrect decisions can have serious consequences. Another limitation is their limited performance with complex interactions between attributes. While decision trees can effectively represent simple relationships between attributes, they may struggle to capture complex interactions, in those cases, other methods might be more applicable. <ref name="Quinlan">J.R. Quinlan,Simplifying decision trees, International Journal of Man-Machine Studies, Volume 27, Issue 3, 1987, Pages 221-234, ISSN 0020-7373, https://doi.org/10.1016/S0020-7373(87)80053-6. </ref>		Decision trees have few disadvantages that can affect their usefulness in certain contexts. One major issue is their tendency to become overly complex, which can make them difficult for experts to interpret and can hinder decision-making processes, especially in high-stakes situations where incorrect decisions can have serious consequences. Another limitation is their limited performance with complex interactions between attributes. While decision trees can effectively represent simple relationships between attributes, they may struggle to capture complex interactions, in those cases, other methods might be more applicable. <ref name="Quinlan">J.R. Quinlan,Simplifying decision trees, International Journal of Man-Machine Studies, Volume 27, Issue 3, 1987, Pages 221-234, ISSN 0020-7373, https://doi.org/10.1016/S0020-7373(87)80053-6. </ref>

S222540 at 14:32, 9 May 2023

2023-05-09T14:32:29Z

← Older revision		Revision as of 14:32, 9 May 2023
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	=='''Annotated bibliography'''==		=='''Annotated bibliography'''==
			'''Kapil Mittal, Dinesh Khanduja and Puran Chandra Tewari. An Insight into “Decision Tree Analysis”. 2017.
			This article discusses the concept of decision tree analysis as a technique to help with decision-making, both in personal and professional contexts. The authors provide an overview of the history and evolution of decision tree analysis and its application in various industries. They also describe the advantages and disadvantages of using this methodology and provide examples to demonstrate the positive effects decision tree analysis has on productivity in an industrial environment.

S222540 at 09:36, 9 May 2023

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← Older revision		Revision as of 09:36, 9 May 2023
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	In conclusion, decision tree analysis is a valuable tool that can help individuals and organizations make more informed decisions. By visually representing the decision-making process and analysing data to estimate the potential outcomes of different decisions, decision tree analysis can assist in project, program, and portfolio management, as well as risk management. Decision tree analysis can help decision-makers identify the best course of action, allocate resources effectively, and mitigate potential risks, making it an invaluable tool in today's complex business environment.		In conclusion, decision tree analysis is a valuable tool that can help individuals and organizations make more informed decisions. By visually representing the decision-making process and analysing data to estimate the potential outcomes of different decisions, decision tree analysis can assist in project, program, and portfolio management, as well as risk management. Decision tree analysis can help decision-makers identify the best course of action, allocate resources effectively, and mitigate potential risks, making it an invaluable tool in today's complex business environment.

			=='''Annotated bibliography'''==

S222540 at 09:35, 9 May 2023

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	'''References'''		=='''References'''==

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	<references />		<references />

S222540 at 09:34, 9 May 2023

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	Furthermore, this article will discuss the limitations and shortcomings of decision tree analysis that need to be kept an eye on when applying the method.		Furthermore, this article will discuss the limitations and shortcomings of decision tree analysis that need to be kept an eye on when applying the method.

			__TOC__

	=='''Big Idea'''==		=='''Big Idea'''==

S222540 at 09:33, 9 May 2023

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	=='''Abstract'''==		=='''Abstract'''==
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	In every persons personal and professional life, they are faced with decisions. Decision-making is a part of every single project, and project managers have numerous tools available to help them with those decisions. A method that is often used is decision tree analysis, which revolves around mapping out the outcomes of possible decisions and how they could determine other outcomes to help project managers and others to make the best possible decision.		In every persons personal and professional life, they are faced with decisions. Decision-making is a part of every single project, and project managers have numerous tools available to help them with those decisions. A method that is often used is decision tree analysis, which revolves around mapping out the outcomes of possible decisions and how they could determine other outcomes to help project managers and others to make the best possible decision.
	The purpose of this article is to show the main concepts of decision tree analysis and teach people how to use it. Making the right decision, or the best decision given the information at hand is a vital part in a project’s managers role. This article will shed light on the steps involved in using the decision tree analysis and how it can be used to guide project managers as well as others into making the best decision possible. The different concepts are discussed, and a step-by-step guidance is introduced.		The purpose of this article is to show the main concepts of decision tree analysis and teach people how to use it. Making the right decision, or the best decision given the information at hand is a vital part in a project’s managers role. This article will shed light on the steps involved in using the decision tree analysis and how it can be used to guide project managers as well as others into making the best decision possible. The different concepts are discussed, and a step-by-step guidance is introduced.