Decision tree analysis

Revision as of 21:36, 5 March 2023

Abstract

A common machine learning algorithm called decision tree analysis is used to categorize and predict outcomes based on a collection of input features ^[1]. 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 ^[2]. It is especially helpful for issues with binary outcomes, like predicting client churn, determining credit risk, and spotting fraud ^[3]. Decision trees are a popular option for outlining the decision-making process to stakeholders because they are simple to understand ^[4].

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 ^[4].

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 ^[4].

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^[5].

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 ^[6](Zhang et al., 2017). By minimizing the detrimental effects of risks on project outcomes, such a strategy can increase the likelihood of achieving project goals.

Application

Real life examples

Finance: In the banking and financial sector, decision tree analysis is used for credit scoring, risk management, and fraud discovery ^{[7] [8]}.

Medicine: decision tree analysis is used to make clinical decisions, diagnose diseases, and determine the fate of patients ^{[9] [10]}.

Marketing: Customer segmentation, marketing targeting, and product recommendation all use decision tree analysis ^{[2] [11]}.

Engineering: In production and engineering, decision tree analysis is used for fault diagnosis, quality control, and process optimization ^{[12] [13]}.

Environmental science: decision tree analysis is used in species distribution modeling, land use planning, and environmental impact evaluation ^{[14] [15]}.

Limitations

Overfitting: Decision trees are susceptible to overfitting, which happens when the model interprets training data as noise rather than the underlying pattern ^{[16] [17]}.

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^{[1] [4]}.

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 ^{[18] [19]}.

Interpretability: When dealing with big or complex trees that have numerous branches or nodes, decision trees can be challenging to understand and interpret ^{[20] [21]}.

References

1. ↑ ^{1.0 1.1} Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Chapman and Hall
2. ↑ ^{2.0 2.1} 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
3. ↑ Wasserman, L. (2013). All of statistics: A concise course in statistical inference. Springer Science & Business Media(Wasserman, 2013)
4. ↑ ^{4.0 4.1 4.2 4.3} Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer
5. ↑ Lev, B., Petrovits, C., & Radhakrishnan, S. (2016). Is doing good good for you? How corporate charitable contributions enhance revenue growth. Strategic Management Journal
6. ↑ Zhang, J., Li, X., He, Y., & Fan, X. (2017). Risk management in construction projects using a combined fuzzy decision-making approach. Sustainability.
7. ↑ Kohavi, R., & Provost, F. (1998). Glossary of terms. Machine learning
8. ↑ 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
9. ↑ 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
10. ↑ 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
11. ↑ Verbeke, W., Dejaeger, K., Martens, D., Hur, J., Baesens, B., & Vanthienen, J. (2014). A novel profit-based classification model for customer base analysis
12. ↑ 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
13. ↑ Chen, G., Gao, X., & Li, C. (2015). Decision tree-based quality control for ultrasonic welding of lithium-ion battery. Journal of Materials Processing Technology.
14. ↑ Figueiredo, R. O., Rocha, J. C. V., & Tavares, R. A. (2019). Decision tree models for environmental impact assessment. Environmental Modelling & Software
15. ↑ 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
16. ↑ Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. Springer
17. ↑ Quinlan, J. R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann
18. ↑ James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. Springer
19. ↑ Zadrozny, B., & Elkan, C. (2002). Transforming classifier scores into accurate multiclass probability estimates. Journal of Machine Learning Research
20. ↑ 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
21. ↑ 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