Decision tree analysis
Contents |
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].
Application
Real life examples
Finance: In the banking and financial sector, decision tree analysis is used for credit scoring, risk management, and fraud discovery [5] [6].
In medicine, decision tree analysis is used to make clinical decisions, diagnose diseases, and determine the fate of patients [7] [8].
Marketing: Customer segmentation, marketing targeting, and product recommendation all use decision tree analysis [2] [9].
Engineering: In production and engineering, decision tree analysis is used for fault diagnosis, quality control, and process optimization (Al-Marwani et al., 2020; Chen et al., 2015).
Environmental science: decision tree analysis is used in species distribution modeling, land use planning, and environmental impact evaluation (Figueiredo et al., 2019; Pu et al., 2020).
Limitations
References
- ↑ Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Chapman and Hall
- ↑ 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
- ↑ Wasserman, L. (2013). All of statistics: A concise course in statistical inference. Springer Science & Business Media(Wasserman, 2013)
- ↑ 4.0 4.1 4.2 Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer
- ↑ Kohavi, R., & Provost, F. (1998). Glossary of terms. Machine learning, 30(2-3), 271-274
- ↑ 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, 78, 15-26
- ↑ 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, 120(8), 13144-13151
- ↑ 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, 94, 97-103
- ↑ Verbeke, W., Dejaeger, K., Martens, D., Hur, J., Baesens, B., & Vanthienen, J. (2014). A novel profit-based classification model for customer base analysis
