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.click for a larger image
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 ?
Decision trees vary primarily in how they choose a OsplitO of the data, and in how they prune the trees they produce.
Decision tree can be used for classification and regression problems, in difference to neuron nets, decision tree generates model that can explain in form of the rules the relation of the incoming and out -coming variables.
Using the expression values of the 108 genes identified in the literature as breast cancer-related as input to the decision tree algorithm, the team analyzed gene expression data from 75 women, 53 of whom had been diagnosed with breast cancer.
Norman Digance, group manager of FSA conduct of business standards, said: 'The good news is that decision trees are liked and understood and can play a useful role in the buying process, provided it is alongside some form of other help.
In the CART algorithm, pruning option to avoid unnecessary nodes in the decision tree diagram was employed.
Based on the decision trees, the interrelationship among the design variables was revealed and the design rules were generated to produce good design sets.
In Data mining algorithms, Decision trees are the best and commonly used approach for representing the data [18].
Analysis of technical actions based on decision tree classification
In Section 2, fast CU size decision algorithm based on decision trees is presented.
Several of them make use of decision trees as prediction models, given a knowledge base, which categorizes a number of conditions to reach the solution of a problem.
The decision trees show that both the two algorithms can determine the dividing point of the variables, especially for the continuous variables based on the training data, which makes the division of the interval more scientific and reduces the segmentation error, compared with the artificial division.
First introduced in 1960's, decision trees are one of the most effective methods for data mining; they have been widely used in several disciplines [l] because they are easy to be used, free of ambiguity, and robust even in the presence of missing values.