Decision trees are simple to use, easy to understand, and offer many advantages compared to other decision-making tools, but they still don't find wide acceptance. Read on to find out the decision tree disadvantages that inhibit its widespread application.
The reliability of the information in the decision tree depends on feeding the precise internal and external information at the onset. Even a small change in input data can at times, cause large changes in the tree. Changing variables, excluding duplication information, or altering the sequence midway can lead to major changes and might possibly require redrawing the tree.
Another fundamental flaw of the decision tree analysis is that the decisions contained in the decision tree are based on expectations, and irrational expectations can lead to flaws and errors in the decision tree. Although the decision tree follows a natural course of events by tracing relationships between events, it may not be possible to plan for all contingencies that arise from a decision, and such oversights can lead to bad decisions.
Decision trees are also prone to errors in classification, owing to differences in perceptions and the limitations of applying statistical tools.
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Among the major decision tree disadvantages are its complexity. Decision trees are easy to use compared to other decision-making models, but preparing decision trees, especially large ones with many branches, are complex and time-consuming affairs.
Computing probabilities of different possible branches, determining the best split of each node, and selecting optimal combining weights to prune algorithms contained in the decision tree are complicated tasks that require much expertise and experience.
Decision trees moreover, examine only a single field at a time, leading to rectangular classification boxes. This may not correspond well with the actual distribution of records in the decision space.
Decision trees, while providing easy to view illustrations, can also be unwieldy. Even data that is perfectly divided into classes and uses only simple threshold tests may require a large decision tree. Large trees are not intelligible, and pose presentation difficulties.
Drawing decision trees manually usually require several re-draws owing to space constraints at some sections, as there is no foolproof way to predict the number of branches or spears that emit from decisions or sub-decisions.
The complexity in creating large decision trees mandates people involved in preparing decision trees having advanced knowledge in quantitative and statistical analysis. This raises the possibility of having to train people to complete a complex decision tree analysis. The costs involved in such training makes decision tree analysis an expensive option, and remains a major reason why many companies do not adopt this model despite its many advantages.
Preparing a decision tree without proper expertise, experience, or knowledge can cause garbled outcome of business opportunities or decision possibilities.
Too Much Information
One of the decision tree advantages are its listing comprehensive information and all possible solutions to an issue. Such comprehensiveness can, however, work both ways and need not always be an advantage. The most significant dangers with such excessive information is "paralysis of analysis" where the decision makers burdened with information overload takes time to process information, slowing down decision-making capacity. The time spent on analysis of various routes and sub routes of the decision trees would find better use by adopting the most apparent course of action straightway and getting on with the core business process, making such information rank along the major disadvantages of a decision tree analysis.
Among the major disadvantages of a decision tree analysis is its inherent limitations. The major limitations include:
- Inadequacy in applying regression and predicting continuous values
- Possibility of spurious relationships
- Unsuitability for estimation of tasks to predict values of a continuous attribute
- Difficulty in representing functions such as parity or exponential size
- Possibility of duplication with the same sub-tree on different paths
- Limited to one output per attribute, and inability to represent tests that refer to two or more different objects
An understanding of the pros and cons of a decision tree analysis reveals that decision tree disadvantages negate much of the advantages, especially in large and complex trees, inhibiting its widespread application as a decision-making tool.