Decision tree analysis

Revision as of 15:34, 6 May 2023

Abstract

In every persons personal and professional life, they are always faced with decisions. These decisions are both small and large and can have great impact on organizations and their goals. Making the “right” decision can often be difficult, which is why numerous tools have been developed over the years to help with this. One of those tools is decision tree analysis.

Decision tree analysis is a method that uses graphical representation to show the decision-making process given certain conditions. It’s most often used to determine the optimal decision based on data and to estimate the potential consequences of a decision when faced different circumstances. The history of decision tree analysis is complex, with several mathematicians and others developing it over time. However, in the industrial environment, decision making usually involves analysing the data to reach a conclusion. Decision tree analysis has, over the years, become increasingly more used in real life, and numerous case studies show its usefulness in improving efficiency in organizations and customer satisfaction. A common use of decision tree analysis is to help with project-selection, which is a necessary part of decision-making process. A number of different instances call for the use of decision tree analysis, and the procedure has bee shown to provide important benefits in a lot of different instances. ^[1]

Decision tree analysis can be used in project, program, and portfolio management to help with the decision-making process in relation to project selection, prioritization as well as risk management. Decision tree analysis can assist managers and other people responsible for making decisions in determining the best course of action and effectively allocate resources by examining many scenarios and potential outcomes.

A common machine learning algorithm called decision tree analysis is used to categorize and predict outcomes based on a collection of input features ^[2]. Each node represents a choice, and each branch represents one or more potential outcomes, creating a tree-like model of decisions and their potential effects. In order to decide what actions to take at each node based on the input features, the algorithm learns from a training collection of labeled data.

Many industries, including banking, medicine, marketing, and engineering, use decision tree analysis ^[3]. It is especially helpful for issues with binary outcomes, like predicting client churn, determining credit risk, and spotting fraud ^[4]. Decision trees are a popular option for outlining the decision-making process to stakeholders because they are simple to understand ^[5].

Decision trees can, however, be vulnerable to overfitting, which can result in subpar performance on new data. To get around this, methods like regularization, ensembling, and trimming can be used to improve the model ^[5].

Pruning is a technique that involves cutting off limbs from the tree that do not increase the model's accuracy when applied to fresh data. A method called assembling joins different decision trees to produce a more reliable model. By penalizing complexity in the tree, regularization keeps it from getting too complicated and overfitting the training data ^[5].

Big Idea

Decision tree analysis is a useful tool in project management that can be used by managers to add value assisting them in making decisions that are well-informed and based on a thorough analysis of the options and related risks. In project management, decision tree analysis is frequently used to compare and choose between various project choices based on their expected value and potential risks.

Project selection is one application of decision tree analysis in project management. Project managers can use decision tree analysis to assess and contrast the anticipated value of each project in terms of benefits, costs, and risks before selecting one over the others. Given the resources and constraints at their disposal, this allows project managers to select the project that will add the most value to the organization. For instance, decision tree analysis can be used to compare the expected return on investment (ROI) and risk profile of each project that a business is considering investing in^[6].

Additionally, decision tree analysis can be used in risk management to assist project managers in recognizing and assessing threats as well as creating efficient defenses. Project managers can prioritize the most important risks and create risk management strategies that aim to reduce their potential effect on the project's success by conducting a thorough analysis of the various risk scenarios using decision trees ^[7]. By minimizing the detrimental effects of risks on project outcomes, such a strategy can increase the likelihood of achieving project goals.

The best way to allocate project resources can be determined using decision tree analysis. Using decision tree analysis, project managers can assess various resource allocation scenarios while taking into account the anticipated value of each option and related risks. As a result, resource allocation can be done by project managers in a manner that maximizes project value and lowers risk ^[8].

Last but not least, decision tree analysis can be used to compare and choose between various project execution options. A project manager might have to decide between using an agile or traditional project management strategy, for instance. Decision tree analysis can be used to compare the anticipated costs, benefits, and risks of each strategy and identify which is most likely to add value to the project^[9].

Application

Real life examples

Finance: In the banking and financial sector, decision tree analysis is used for credit scoring, risk management, and fraud discovery ^{[10] [11]}.

Medicine: decision tree analysis is used to make clinical decisions, diagnose diseases, and determine the fate of patients ^{[12] [13]}.

Marketing: Customer segmentation, marketing targeting, and product recommendation all use decision tree analysis ^{[3] [14]}.

Engineering: In production and engineering, decision tree analysis is used for fault diagnosis, quality control, and process optimization ^{[15] [16]}.

Environmental science: decision tree analysis is used in species distribution modeling, land use planning, and environmental impact evaluation ^{[17] [18]}.

Limitations

Overfitting: Decision trees are susceptible to overfitting, which happens when the model interprets training data as noise rather than the underlying pattern ^{[19] [20]}.

Instability: Decision trees are sensitive to small changes in the data or model parameters and can be unstable. This can lead to various tree structures or predictions^{[2] [5]}.

Bias: Decision trees may be biased toward factors with a high complexity or number of categories, which could lead to an over- or under-representation of particular categories ^{[21] [22]}.

Interpretability: When dealing with big or complex trees that have numerous branches or nodes, decision trees can be challenging to understand and interpret ^{[23] [24]}.

References

1. ↑ Kapil Mittal, Dinesh Khanduja, Puran Chandra Tewari. (2017). An Insight into “Decision Tree Analysis”. WWJMRD, 3(12), 111-115
2. ↑ ^{2.0 2.1} Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Chapman and Hall
3. ↑ ^{3.0 3.1} Kotsiantis, S. B., Zaharakis, I. D., & Pintelas, P. E. (2006). Machine learning: a review of classification and combining techniques. Artificial Intelligence Review, 26(3), 159-190
4. ↑ Wasserman, L. (2013). All of statistics: A concise course in statistical inference. Springer Science & Business Media(Wasserman, 2013)
5. ↑ ^{5.0 5.1 5.2 5.3} Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer
6. ↑ Lev, B., Petrovits, C., & Radhakrishnan, S. (2016). Is doing good good for you? How corporate charitable contributions enhance revenue growth. Strategic Management Journal
7. ↑ Zhang, J., Li, X., He, Y., & Fan, X. (2017). Risk management in construction projects using a combined fuzzy decision-making approach. Sustainability.
8. ↑ Hwang, B. G., & Ng, W. J. (2013). Project management using a decision tree model for handling risks in construction projects. Automation in Construction.
9. ↑ Buddhika, A., Janaka, S., & Jayasena, H. (2019). Decision tree analysis in agile project management. In 2019 IEEE 9th International Conference on Advanced Computing.
10. ↑ Kohavi, R., & Provost, F. (1998). Glossary of terms. Machine learning
11. ↑ Tang, F., Zeng, G., Deng, L., Huang, G., Li, X., & Wang, X. (2015). Decision tree models for effective credit scoring in peer-to-peer online microloan platforms. Decision Support Systems
12. ↑ Chen, J., Guo, Y., Li, S., Li, J., & Li, J. (2019). A decision tree approach to predicting the survival of gastric cancer patients. Journal of Cellular Biochemistry
13. ↑ Leite, F. N., Oliveira, C. A., Cunha, A. M., Körbes, D., Fumagalli, F., & Leite, J. S. (2018). Decision trees for predicting breast cancer recurrence using clinical data. Expert Systems with Applications
14. ↑ Verbeke, W., Dejaeger, K., Martens, D., Hur, J., Baesens, B., & Vanthienen, J. (2014). A novel profit-based classification model for customer base analysis
15. ↑ Al-Marwani, A., Ramachandran, M., & Subramanian, R. (2020). A review on the application of decision tree and random forest algorithms in engineering. Journal of Advanced Research in Dynamical and Control Systems
16. ↑ Chen, G., Gao, X., & Li, C. (2015). Decision tree-based quality control for ultrasonic welding of lithium-ion battery. Journal of Materials Processing Technology.
17. ↑ Figueiredo, R. O., Rocha, J. C. V., & Tavares, R. A. (2019). Decision tree models for environmental impact assessment. Environmental Modelling & Software
18. ↑ Pu, J., Tang, Q., & Yao, X. (2020). A comparative study of decision tree algorithms for modeling the spatial distribution of forest soil nutrients. Science of The Total Environment
19. ↑ Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. Springer
20. ↑ Quinlan, J. R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann
21. ↑ James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. Springer
22. ↑ Zadrozny, B., & Elkan, C. (2002). Transforming classifier scores into accurate multiclass probability estimates. Journal of Machine Learning Research
23. ↑ Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015). Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
24. ↑ Lakkaraju, H., Bach, S. H., & Leskovec, J. (2016). Interpretable decision sets: A joint framework for description and prediction. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining