Attached. Please let me know if you have any questions or need revisions.1Decision Tree ConceptsStudent’s NameInstitutional AffiliationCourse NumberInstructor’s NameDate2Decision TreesIntroductionDecision trees are tree-like decision support tools that can model things such as possibleconsequences, probable outcomes, utilities, and cost of resources. Decision trees make itpossible to present conditional control statements in the form of algorithms. They comprisedecision-making steps – represented in the form of branches, which aid in arriving at favourableresults. The focus of this paper is to explain several concepts related to decision trees. Importantconcepts that will be covered include the decision tree algorithm, types of decision trees,important terminologies, assumptions made when creating decision trees, attributes selectionmethods, different applications, and advantages and disadvantages. But first, an explanation willbe provided on how to choose between tree and linear-based modelsChoosing between Tree and Linear-Based ModelsThe model to be used to make a decision depends on the problem being solved. Forinstance, if linear models predict the relationship between independent and dependent variableswith higher accuracy, then a linear model should be chosen over a tree-based model. However,tree-based models should be used when the relationship between independent and dependentvariables exhibit high non-linearity. But if the need is to build a simpler model that is easy tointerpret, decision trees will always outperform linear models (Klosterman, 2019).Decision Tree AlgorithmAccording to Chauhan (n.d.), decision tree algorithms can solve classification andregression problems. Decision trees are used to create training models for predicting values orclasses or target variables; this is achieved by inferring simple decision rules from past data. Topredict class labels using decision trees, one must begin from the root of the tree. Values of the3root attribute are then compared to record attributes. Comparison is done by following thebranches that correspond to root attribute values and then jumping to the next node.Types of Decision TreesThe two decision tree types are continuous variable and categorical variable decisiontrees. Continuous variable decision trees have continuous target variables (Corporate FinanceInstitute, n.d.). On the other hand, categorical variable decision trees have categorical t…
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