A decision tree is a tree where every branch node symbolizes an option among several choices, and each leaf node represents a classification or decision. Decision Trees will be superb tools for assisting you to make a choice from numerous courses of action.

Decision Tree Examples

Decision trees may be used to enhance an expense portfolio. This example demonstrates a portfolio of 7 investment options (projects). This company has $10, 000, 000 accessible for the investment. Bold lines mark the best selection 1, 3, 5, 6, and 7, which will cost $9, 750, 000 and create a payoff of 16, 175, 000. All the other mixtures will either surpass the budget allowed or even produce less payoff.

You start a decision tree with a decision that you need to make. Draw a small square to represent this towards the left of a large piece of paper.

Then take the lines one at a time. At the end of the line, do you get a particular result, or is it uncertain or is there another decision to be made? If it is another decision, draw a square. If uncertain, a circle, and if a result, draw nothing.

Review each square and circle. For the squares (decisions), draw lines for the choices, marking them in as you go. For the circles (uncertainties) draw further lines for the possible outcomes. Keep going until you have filled out the possibilities leading from your original decision.

You will have something similar to the first of the decision tree diagrams.

Now it’s time to work out which option is most valuable to you. First, estimate how much each option would be worth to you.

Then review each circle/point of uncertainty. Here you determine the probability of each outcome. Make sure percentages add up to 100, or fractions amount to a total of 1. Your decision tree diagrams will now look something like this.

Start at the left hand values and work to the right. At any circles, multiply the end value by the probability of it occurring. So in our example, the probability of promotion is 0.7. Multiply this by 80,000 to get 56,000.

Each of the end values is recalculated in this way. It’s also useful to add in any costs that will be incurred along the way. This gives a more accurate picture of the net value.

Another example:

Amongst decision support tools, decision trees have several advantages:

  • Are simple to understand and interpret. People are able to understand decision tree models after a brief explanation.
  • Have value even with little hard data. Important insights can be generated based on experts describing a situation (its alternatives, probabilities, and costs) and their preferences for outcomes.
  • Use a white box model. If a given result is provided by a model, the explanation for the result is easily replicated by simple math.
  • Can be combined with other decision techniques.

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