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IREUS Working Paper Series 2020Decision tree and Boosting Techniques in Artificial Intelligence Based Automated Valuation Models (AIAVM)Tien Foo Sing Jesse Jingye Yang Shi Ming YuMay 19, 2020WP20200506Decision
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Gather all relevant data for the decision tree
02
Determine the target variable you want to predict
03
Split the data into training and testing sets
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Choose a decision tree algorithm to use
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Train the decision tree on the training set
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Decision trees from efficient are graphical representations used to make decisions based on various input variables, optimizing outcomes based on efficiency criteria.
Individuals or organizations that engage in decision-making processes that necessitate the analysis of various options and outcomes based on efficiency are required to file decision trees from efficient.
To fill out decision trees from efficient, you need to identify the decision points, possible outcomes, and probabilities, and then systematically lay out the information in a tree format.
The purpose of decision trees from efficient is to assist stakeholders in visually analyzing decisions and their potential consequences to promote better decision-making.
Decision trees from efficient must report the decision criteria, potential alternatives, expected outcomes, associated probabilities, and any relevant efficiency metrics.
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