Decision tree analysis
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| − | A common machine learning algorithm called decision tree analysis is used to categorize and predict outcomes based on a collection of input features <ref name="Breiman">Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Chapman and Hall</ref> | + | A common machine learning algorithm called decision tree analysis is used to categorize and predict outcomes based on a collection of input features <ref name="Breiman">Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Chapman and Hall</ref>. 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 (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). | + | Many industries, including banking, medicine, marketing, and engineering, use decision tree analysis <ref name="Kotsiantis">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<\ref>. (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). |
Revision as of 15:11, 19 February 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]
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