Decision Trees: Exploring Modeling, Benefits, and SmartArt Tool Use

This essay discusses the use of decision trees as diagrams or flowcharts that aid in exploring decision alternatives and available options. It highlights the importance of decision trees in modeling, particularly in creating visual representations of decisions and outcomes, feature selection, and data interpretation. The essay also explores the benefits of using the SmartArt tool in Excel for creating decision trees, emphasizing its ease of use, ability to specify values, graphical illustration capabilities, and handling of categorical and continuous variables. Despite the advantages, the essay acknowledges the disadvantages of decision trees, such as the constant changes in Excel features and the potential for management bias. The document concludes by referencing relevant academic literature supporting the arguments presented.

Running head: DECISION TREES
Discussion 1: Decision Trees
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Introduction
Decision trees are diagrams or flowchart which help in exploring all the decision
alternatives and any possible option that is available in making a certain decision. Each and
every branch in a decision tree usually represents an available option in making a certain
decision. Computer science modelers define decision tree as an analysis program. According to
the model blog, a decision tree gives a decision maker an overview of the various stages which
follows each and every decision. In business, decision trees are used to help in forming a
balanced picture of the organization rewards and risks (Quinlan, 2017).
Importance of decision trees in modeling
Decision trees usually assist managers in evaluating the upcoming choices. Decision trees
assist in modeling by creating a visual representation of all possible decisions and outcomes. In
modeling, decision trees are used to perform to perform feature selection. In here, there are used
to perform a variable screening. Decisions trees require very little effort when performing data
interpretation and this is one of the main reasons as to why data modeler use when manipulating
data. It usually saves time for data modelers. As a tree-like graph, data modelers’ uses decision
trees to find out the possible consequences which also include resource costs and data utility. In
addition, decision trees are used in operations management (Lloyd, 2011). Data modelers also
use as a descriptive tool to calculate conditional probabilities. Other advantages of decision trees
is that there very simple to interpret and understand. They also help in understanding the
expected values of a certain scenario. Decisions trees also help in coming up with important
insights of a certain situation. They can also be combined with other decisions techniques
(Warnes, 2018).

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Benefits of using SmartArt tool in creating decision trees in excel
There various add-ins and tools in Excel; one of the tools which has been highly utilized
when creating decision trees is SmartArt. One of the advantage of this tool is that it is very easy
to use and un-like other modelling tools, excel is transparency in nature. Second, this tool has the
ability of specifying a specific value to a problem which in turn reduces its ambiguity in
decision-making. The tool also provides a graphical illustration of a certain problem. This means
any person who have no knowledge of excel, when presented with the solution, they are able to
understand. In addition, excel is able to handle both categorical and continuous variables. Lastly
with, with Smart Art tool one is able to set a certain problem clearly and it also encourages a
logical approach to the data. Even though decision trees are used they one major disadvantages;
the tools implanted used such excel constantly change due to the continuous added features. In
addition, decision trees are management bias (Lindley, 2013).

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References
Lindley, D. V. (2013). Making decisions. New York: Wiley.
Lloyd, C. J. (2011). Data-driven business decisions by Chris J Lloyd . Chicago: Wiley.
Quinlan, J. R. (2017). Introduction of decision trees. Machine learning, 81-106.
Warnes, M. R. (2018). Data-based modelling techniques. Decisions Trees, 147-155.