Decision tree

Decision tree
See also
Decision point Algorithm Feature-driven development DMAIC methodology Linear programming Concept engineering Behavior driven development Descriptive model Model of creativity

A decision tree is a graphical way to support the decision-making process. The tree is used in decision theory, and has a lot of uses. It can help in solving the decision problem, and creating a plan. Decision trees are useful in problems with many decision variants and in situation of high risk. Decision trees are used in fields such as botany and medicine. Increasingly, they are used also in economics, as they are able to facilitate and streamline computer-aided decision-making process.

Application of decision trees

Decision trees technique allow to:

• decide on decision-making rules that describe assigning objects to particular classes,
• analyze the set of objects described by the adopted set of attributes,
• improvement of the distribution of objects in homogeneous classes.

The starting point is a collection that contains all the analyzed objects; in the course of the analysis it is split into a number of subsets. Next each of the subsets is further divided; at the end of the analysis each object is a separate class.

Fig. 1. Sample decision tree of customer decision making process

Elements of decision tree analysis

A decision tree is consists of a root, nodes, and leaves. Leaves are nodes that do not have edges. The root of the tree is created by the selected attribute, while the individual branches represent the value of this attribute. Thanks to the decision tree, created on the basis of empirical data, you can classify new objects that were not available in time of creating the tree.

Decision trees are characterized by a hierarchical structure. During following steps set of objects is divided into smaller sets on basis of values of the selected features or a combination of them. The final decision depends on the answers to all questions. In the tree construction algorithms, one of the key elements is the choice of sequence of features, according to which, on various stages, set of objects will be divided. Decision trees is a complement to the classical decision making methods. An example might be discriminant analysis. Decision tree hierarchy is a feature that stands out from the other decision making methods.

Basic rules for decision tree design

The general rules of decision tree design are:

1. Examine whether a set of objects is homogeneous. If it is, algorithm stops. If not, algorithm continues
2. Examining all possible splits of set of objects to identify which of the divisions are most homogeneous.
3. Division of set on the basis of adopted criterion.
4. Use of this algorithm for all subsets of objects.
5. Categorization of tree, involving winding-up of the parts of the tree with a small importance to the quality of the results.
6. Use of tree for classification of new objects.

Examples of Decision tree

• Business: A business can use a decision tree to determine the best course of action when dealing with a customer complaint. By creating a tree, the business can identify potential solutions, weigh their pros and cons, and decide on the best option.
• Medicine: A doctor can use a decision tree to assess a patient's symptoms and determine the most appropriate diagnosis and course of treatment. By inputting the patient's symptoms and medical history into the tree, the doctor can determine the most likely diagnosis, as well as the best treatment plan.
• Education: Educators can use decision trees to determine the best course of action for a student based on their academic performance. By using a decision tree, the educator can identify various interventions that could help the student improve their performance, and determine which one is the most effective.
• Manufacturing: Manufacturers can use decision trees to determine the most efficient process for producing a product. By creating a tree, the manufacturer can identify potential production methods, compare their costs and benefits, and decide on the most cost-effective option.

Advantages of Decision tree

A decision tree is a graphical way to support the decision-making process, with a wide range of uses in fields such as botany, medicine, and economics. There are several advantages to using a decision tree, such as:

• Increased clarity when it comes to the decision-making process, as the tree provides a visual representation of the various possible choices.
• The ability to identify the most appropriate solution or outcome quickly, since decision trees allow for a systematic evaluation of each option.
• The ability to identify potential risks or drawbacks associated with each decision, allowing for more informed decisions.
• Decision trees can be used to compare different outcomes to determine which one would be the most beneficial.
• They provide an effective way to communicate decisions to others, as the tree structure makes the decision-making process clear and easy to understand.

Limitations of Decision tree

• Decision trees can be biased by outliers, or an over-representation of certain classes.
• They can also be unstable, meaning small changes in data can result in a different decision tree.
• Decision trees can be difficult to interpret and explain, as the results are not always easily understood.
• Decision trees can also be computationally expensive, as they require a lot of calculations.
• They may also fail to capture all possible scenarios, as they are limited by the data provided.

Other approaches related to Decision tree

• Decision Analysis: Decision analysis is a systematic approach to making decisions based on a set of criteria. It involves summarizing the data, making assumptions, and then evaluating the potential outcomes.
• Artificial Intelligence: Artificial intelligence is the process of creating systems that can think and act like humans. This can be used to help make decisions, by analyzing large amounts of data and providing insights.
• Utility Theory: Utility theory is the idea that individuals make decisions that maximize their own satisfaction. This is done by assigning a numerical value to the different options and then using a mathematical formula to determine the best option.
• Bayesian Networks: Bayesian networks are a type of probabilistic graphical model that can be used to infer the probability of different outcomes.

In conclusion, decision trees are a graphical way to support the decision-making process. Other approaches related to decision trees include decision analysis, artificial intelligence, utility theory, and Bayesian networks. Each of these approaches has its own strengths and weaknesses, and can be used in different situations to make decisions more effectively.

References

• Magee, J. F. (1964). Decision trees for decision making. Harvard Business Review.
• Quinlan, J. R. (1990). Decision trees and decision-making. Systems, Man and Cybernetics, IEEE Transactions on, 20(2), 339-346.
• Rokach, L., & Maimon, O. (2014). Data mining with decision trees: theory and applications. World scientific.