Decision tree analysis
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]. (Kotsiantis et al., 2006). It is especially helpful for issues with binary outcomes, like predicting client churn, determining credit risk, and spotting fraud (Wasserman, 2013).
Application
Limitations
References
- ↑ Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Chapman and Hall
- ↑ 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
Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Chapman and Hall.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
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.
