Advantages & Disadvantages of Decision Trees

Written by gregory hamel
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Decision trees are diagrams that attempt to display the range of possible outcomes and subsequent decisions made after an initial decision. For example, your original decision might be whether to attend college, and the tree might attempt to show how much time would be spent doing different activities and your earning power based on your decision. There are several notable pros and cons to using decision trees.

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Considering Consequences

One of the most useful aspects of decision trees is that they force you to consider as many possible outcomes of a decision as you can think of. It can be dangerous to make spur-of-the-moment decisions without considering the range of consequences. A decision tree can help you weigh the likely consequences of one decision against another. In some cases, it can even help you estimate expected payoffs of decisions. For example, if you create dollar value estimates of all outcomes and probabilities associated with each outcome you can use those numbers to calculate which initial decision will lead to the greatest average financial payoff. Decision trees provide a framework to consider the probability and payoffs of decisions, which can help you analyse a decision to make the most informed decision possible.

Expectations

A drawback of using decision trees is that the outcomes of decisions, subsequent decisions and payoffs may be based primarily on expectations. When actual decisions are made, the payoffs and resulting decisions may not be the same as those you've planned for. It may be impossible to plan for all contingencies that can arise as a result of a decision. This can lead to an unrealistic decision tree that could guide you toward a bad decision. Also, unexpected events may alter decisions and change the payoffs in a decision tree. For example, if you expect that your parents will pay for half of your college when deciding to go to school, but later discover that you will have to pay for all of your tuition, your expected payoffs will be dramatically different than reality.

Complexity

Decision trees are relatively easy to understand when there are few decisions and outcomes included in the tree. Large trees that include dozens of decision nodes (spots where new decisions are made) can be convoluted and may have limited value. The more decisions there are in a tree, the less accurate any expected outcomes are likely to be. For instance, if you make a tree mapping out the decision to go to college, you probably won't be able to accurately predict the chances that you will be making over £65,000 in ten years, but you might be able to accurately estimate your earning power after you get out of college.

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