# decision tree

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## Decision tree

Schematic way of representing alternative sequential decisions and the possible outcomes from these decisions.

## Decision Tree

In risk analysis, a diagram of decisions and their potential consequences. It is used to help determine the most straightforward (and cheapest) way to arrive at a stated goal. It is represented by potential decisions (drawn as squares), branching off into different proximate consequences (drawn as circles), and potential end results (drawn as triangles).
Fig.32 Decision tree. The businessman has two options: to open a new factory to boost production capacity or not to open a new factory; and he has to consider two states of nature or events which can occur economic boom or recession. The businessman must assess the likelihood of each of these events occurring and, in this case, based on his knowledge and experience, he estimates that there is a one-in-two chance of a boom and a 0.5 probability of a recession. Finally, the businessman estimates the financial consequences as an £80,000 profit for the new factory if there is a boom, and a £30,000 loss if there is a recession.

## decision tree

an aid to decision-making in uncertain conditions, that sets out alternative courses of action and the financial consequences of each alternative, and assigns subjective probabilities to the likelihood of future events occurring. For, example, a firm thinking of opening a new factory the success of which will depend upon consumer spending (and thus the state of the economy) would have a decision tree like Fig. 32.

In order to make a decision, the manager needs a decision criterion to enable him to choose which he regards as the best of the alternatives and, since these choices involve an element of risk, we therefore need to know something about his attitudes to risk. If the manager were neutral in his attitude to risk then we could calculate the certainty equivalent of the ‘open factory’ alternative using the expected money value criterion, which takes the financial consequence of each outcome and weights it by the probability of its occurrence, thus:

which being greater than the £0 for certain of not opening the factory would justify going ahead with the factory project.

However, if the manager were averse to risk then he might not regard the expected money value criterion as being appropriate, for he might require a risk premium to induce him to take the risk. Application of a more cautious certainty equivalent criterion would reduce the certainty equivalent of the ‘open factory’ branch and might even tip the decision against going ahead on the grounds of the ‘downside risk’ of losing £30,000.See UNCERTAINTY AND RISK.

## decision tree

a graphical representation of the decision-making process in relation to a particular economic decision. The decision tree illustrates the possibilities open to the decision-maker in choosing between alternative strategies. It is possible to specify the financial consequence of each ‘branch’ of the decision tree and to gauge the PROBABILITY of particular events occurring that might affect the consequences of the decisions made. See RISK AND UNCERTAINTY.
References in periodicals archive ?
Table-II: Performance Criteria for Decision Tree Algorithms (10-fold cross validation).
When looking at the decision tree diagram of CHAID algorithm, it was understood that breed factor was found as the premier factor influencing CCW trait (F=30.
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Decision tree are great tools for classification and prediction in the area of data mining and it represents rules and it is a classifier in the form of a tree structure where each node is either a leaf node, indicating a class of instances.
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In Section 2, fast CU size decision algorithm based on decision trees is presented.
Omid, "Design of an expert system for sorting pistachio nuts through decision tree and fuzzy logic classifier", Expert Syst.
CART decision tree algorithm [39] can divide the sample set into two subsample sets, making the root and intermediate nodes with two branches based on the recursively binary segmentation technology.
After 4 weeks the decision tree group cut the average number of cigarettes smoked daily significantly more than the standard care group (10 versus 6 cigarettes per day) (Figure 1).
In these circumstances, decision tree models can help in deciding how to best collapse categorical variables into a more manageable number of categories or how to subdivide heavily skewed variables into ranges.
This may work nicely for decision trees and concept maps, but for other uses, you may want to convert your Prezi to a video.
A thorough interpretation of the decision tree reveals that the classifier exposes patterns of actions that would be otherwise hard to predict.

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