Real-world applications of the decision tree tool include engineering, civil engineering, law, and business. Categorical variable and continuous variable decision trees are the two categories into which decision trees may be split.
What are some uses for decision trees?
- Effective non-linear data management is accomplished using decision trees. Real-world applications of the decision tree tool include engineering, civil engineering, law, and business. Categorical variable and continuous variable decision trees are the two categories into which decision trees may be split.
- As non-leaf nodes always have two children (i.e., queries can only have yes/no responses), the CART algorithm only creates binary trees. Other Tree methods, such as ID3, on the other hand, can create Decision Trees with nodes having more than two children.
- Decision trees can be used in situations when the result variable is categorical. These apps create decision trees they refer to as "BLANK" trees.
- The DecisionTreeClassifier's entire tree structure is a binary tree, as can be shown from this sklearn page or my experiment. Each Decision TreeClassifier node can only have zero, one, or two child nodes, depending on the criterion—entropy or gini.
To learn more about Decision trees refer to:
https://brainly.com/question/26675617
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